Geir Olve Storvik

• Tandon, Kushagri; Thorarinsdottir, Thordis Linda & Storvik, Geir Olve (2026). Distributional properties of non-Gaussian spatial fields. In Bin, Riccardo De; Salucci, Clara Bertinelli & Hubin, Aliaksandr (Ed.), Proceedings of the 40th International Workshop on Statistical Modelling 28.06 – 03.07.2026, Oslo, Norway. International Workshop on Statistical Modelling. ISSN 9788255325000. p. 526–529. Full text in Research Archive
• Frommlet, Florian; Lachmann, Jon; Storvik, Geir Olve & Hubin, Aliaksandr (2026). FBMS: An R package for flexible Bayesian model selection and averaging with nonlinear models. Journal of Computational Science. ISSN 1877-7503. 100, p. 102947–102947. doi: 10.1016/j.jocs.2026.102947. Full text in Research Archive Show summary

  The FBMS R package provides a unified interface for Bayesian model selection and model averaging across a broad class of regression models, including Gaussian regression, generalized linear models, and models with nonlinear functional relationships, with possible extensions to mixed-effects and survival models. Users specify candidate predictors together with transformation and interaction rules, and FBMS automatically explores the corresponding model spaces, estimates marginal likelihoods, and returns posterior model and inclusion probabilities, best and median probability models, posterior modes of parameters, and predictions with associated uncertainty. The core machinery combines mode-jumping Markov chain Monte Carlo with a genetically modified extension that iteratively generates and evaluates nonlinear features in the framework of Bayesian generalized nonlinear regression, while supporting flexible prior specification (including g-priors, Jeffreys priors, and empirical Bayes constructions), subsampling for large datasets, parallel computation, and the option to supply custom likelihoods for specialized applications. As special cases, the framework covers models based on fractional polynomials, logic regression, neural networks, symbolic regression, and classical linear regression.

• Skaaret-Lund, Lars; Storvik, Geir Olve & Hubin, Aliaksandr (2024). Sparsifying Bayesian neural networks with latent binary variables and normalizing flows. Transactions on Machine Learning Research (TMLR). 2024. doi: 10.48550/arXiv.2305.03395. Full text in Research Archive Show summary

  Artificial neural networks are powerful machine learning methods used in many modern applications. A common issue is that they have millions or billions of parameters and tend to overfit. Bayesian neural networks (BNN) can improve on this since they incorporate parameter uncertainty. Latent binary Bayesian neural networks (LBBNN) further take into account structural uncertainty by allowing the weights to be turned on or off, enabling inference in the joint space of weights and structures. Mean-field variational inference is typically used for computation within such models. In this paper, we will consider two extensions of variational inference for the LBBNN: Firstly, by using the local reparametrization trick (LCRT), we improve computational efficiency. Secondly, and more importantly, by using normalizing flows on the variational posterior distribution of the LBBNN parameters, we learn a more flexible variational posterior than the mean field Gaussian. Experimental results on real data show that this improves predictive power compared to using mean field variational inference on the LBBNN method, while also obtaining sparser networks. We also perform a simulation study, where we consider variable selection in a logistic regression setting with highly correlated data, where the more flexible variational distribution improves results.

• Hubin, Aliaksandr & Storvik, Geir Olve (2024). Sparse Bayesian Neural Networks: Bridging Model and Parameter Uncertainty through Scalable Variational Inference. Mathematics. 12(6). doi: 10.3390/math12060788. Full text in Research Archive Show summary

  Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using a Bayesian approach: parameter and prediction uncertainties become easily available, facilitating more rigorous statistical analysis. Furthermore, prior knowledge can be incorporated. However, the construction of scalable techniques that combine both structural and parameter uncertainty remains a challenge. In this paper, we apply the concept of model uncertainty as a framework for structural learning in BNNs and, hence, make inferences in the joint space of structures/models and parameters. Moreover, we suggest an adaptation of a scalable variational inference approach with reparametrization of marginal inclusion probabilities to incorporate the model space constraints. Experimental results on a range of benchmark datasets show that we obtain comparable accuracy results with the competing models, but based on methods that are much more sparse than ordinary BNNs.

• Storvik, Geir Olve; Palomares, Alfonso Diz-Lois; Engebretsen, Solveig; Rø, Gunnar Øyvind Isaksson; Engø-Monsen, Kenth & Kristoffersen, Anja Bråthen [Show all 8 contributors for this article] (2023). A sequential Monte Carlo approach to estimate a time-varying reproduction number in infectious disease models: the Covid-19 case. Journal of the Royal Statistical Society, Series A (Statistics in Society). ISSN 0964-1998. 186(4), p. 616–632. doi: 10.1093/jrsssa/qnad043. Full text in Research Archive Show summary

  Abstract The Covid-19 pandemic has required most countries to implement complex sequences of non-pharmaceutical interventions, with the aim of controlling the transmission of the virus in the population. To be able to take rapid decisions, a detailed understanding of the current situation is necessary. Estimates of time-varying, instantaneous reproduction numbers represent a way to quantify the viral transmission in real time. They are often defined through a mathematical compartmental model of the epidemic, like a stochastic SEIR model, whose parameters must be estimated from multiple time series of epidemiological data. Because of very high dimensional parameter spaces (partly due to the stochasticity in the spread models) and incomplete and delayed data, inference is very challenging. We propose a state-space formalization of the model and a sequential Monte Carlo approach which allow to estimate a daily-varying reproduction number for the Covid-19 epidemic in Norway with sufficient precision, on the basis of daily hospitalization and positive test incidences. The method was in regular use in Norway during the pandemics and appears to be a powerful instrument for epidemic monitoring and management.

• Lachmann, Jon; Storvik, Geir Olve; Frommlet, Florian & Hubin, Aliaksandr (2022). A subsampling approach for Bayesian model selection. International Journal of Approximate Reasoning. ISSN 0888-613X. 151, p. 33–63. doi: 10.1016/j.ijar.2022.08.018. Full text in Research Archive Show summary

  It is common practice to use Laplace approximations to decrease the computational burden when computing the marginal likelihoods in Bayesian versions of generalised linear models (GLM). Marginal likelihoods combined with model priors are then used in different search algorithms to compute the posterior marginal probabilities of models and individual covariates. This allows performing Bayesian model selection and model averaging. For large sample sizes, even the Laplace approximation becomes computationally challenging because the optimisation routine involved needs to evaluate the likelihood on the full dataset in multiple iterations. As a consequence, the algorithm is not scalable for large datasets. To address this problem, we suggest using stochastic optimisation approaches, which only use a subsample of the data for each iteration. We combine stochastic optimisation with Markov chain Monte Carlo (MCMC) based methods for Bayesian model selection and provide some theoretical results on the convergence of the estimates for the resulting time-inhomogeneous MCMC. Finally, we report results from experiments illustrating the performance of the proposed algorithm.

• Hubin, Aliaksandr; Storvik, Geir & Frommlet, Florian (2021). Flexible Bayesian Nonlinear Model Configuration. The journal of artificial intelligence research. ISSN 1076-9757. 72, p. 901–942. doi: 10.1613/JAIR.1.13047. Full text in Research Archive Show summary

  Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. Such relationships can be better described through flexible approaches such as neural networks, but this results in less interpretable models and potential overfitting. Alternatively, specific parametric nonlinear functions can be used, but the specification of such functions is in general complicated. In this paper, we introduce a flexible approach for the construction and selection of highly flexible nonlinear parametric regression models. Nonlinear features are generated hierarchically, similarly to deep learning, but have additional flexibility on the possible types of features to be considered. This flexibility, combined with variable selection, allows us to find a small set of important features and thereby more interpretable models. Within the space of possible functions, a Bayesian approach, introducing priors for functions based on their complexity, is considered. A genetically modified mode jumping Markov chain Monte Carlo algorithm is adopted to perform Bayesian inference and estimate posterior probabilities for model averaging. In various applications, we illustrate how our approach is used to obtain meaningful nonlinear models. Additionally, we compare its predictive performance with several machine learning algorithms.

• Hubin, Aliaksandr; Frommlet, Florian & Storvik, Geir Olve (2021). Reversible genetically modified mode jumping MCMC. In Makridis, Andreas; Milienos, Fotios; Papastamoulis, Panagiotis; Parpoula, Christina & Rakitzis, Athanasios (Ed.), 22nd European Young Statisticians Meeting – Proceedings. Department of Psychology & Department of Sociology, School of Social Science, Panteion University of Social and Political Sciences. ISSN 9789607943231. p. 35–40. Full text in Research Archive Show summary

  In this paper, we introduce a reversible version of a genetically modified mode jumping Markov chain Monte Carlo algorithm (GMJMCMC) for inference on posterior model probabilities in complex model spaces, where the number of explanatory variables is prohibitively large for classical Markov Chain Monte Carlo methods. Unlike the earlier proposed GMJMCMC algorithm, the introduced algorithm is a proper MCMC and its limiting distribution corresponds to the posterior marginal model probabilities in the explored model space under reasonable regularity conditions.

• Gramuglia, Emanuele; Storvik, Geir Olve & Stakkeland, Morten (2021). Clustering and automatic labelling within time series of categorical observations - with an application to marine log messages. Journal of the Royal Statistical Society, Series C (Applied Statistics). ISSN 0035-9254. 70(3), p. 714–732. doi: 10.1111/rssc.12483. Full text in Research Archive Show summary

  System logs or log files containing textual messages with associated time stamps are generated by many technologies and systems. The clustering technique proposed in this paper provides a tool to discover and identify patterns or macrolevel events in this data. The motivating application is logs generated by frequency converters in the propulsion system on a ship, while the general setting is fault identification and classification in complex industrial systems. The paper introduces an offline approach for dividing a time series of log messages into a series of discrete segments of random lengths. These segments are clustered into a limited set of states. A state is assumed to correspond to a specific operation or condition of the system, and can be a fault mode or a normal operation. Each of the states can be associated with a specific, limited set of messages, where messages appear in a random or semi‐structured order within the segments. These structures are in general not defined a priori. We propose a Bayesian hierarchical model where the states are characterised both by the temporal frequency and the type of messages within each segment. An algorithm for inference based on reversible jump MCMC is proposed. The performance of the method is assessed by both simulations and operational data.

• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2020). Rejoinder for the discussion of the paper "A Novel Algorithmic Approach to Bayesian Logic Regression". Bayesian Analysis. ISSN 1936-0975. 15(1), p. 312–333. doi: 10.1214/18-ba1141. Full text in Research Archive Show summary

  Logic regression was developed more than a decade ago as a tool to construct predictors from Boolean combinations of binary covariates. It has been mainly used to model epistatic effects in genetic association studies, which is very appealing due to the intuitive interpretation of logic expressions to describe the interaction between genetic variations. Nevertheless logic regression has (partly due to computational challenges) remained less well known than other approaches to epistatic association mapping. Here we will adapt an advanced evolutionary algorithm called GMJMCMC (Genetically modified Mode Jumping Markov Chain Monte Carlo) to perform Bayesian model selection in the space of logic regression models. After describing the algorithmic details of GMJMCMC we perform a comprehensive simulation study that illustrates its performance given logic regression terms of various complexity. Specifically GMJMCMC is shown to be able to identify three-way and even four-way interactions with relatively large power, a level of complexity which has not been achieved by previous implementations of logic regression. We apply GMJMCMC to reanalyze QTL (quantitative trait locus) mapping data for Recombinant Inbred Lines in Arabidopsis thaliana and from a backcross population in Drosophila where we identify several interesting epistatic effects. The method is implemented in an R package which is available on github.

• Hubin, Aliaksandr; Storvik, Geir Olve; Grini, Paul Eivind & Butenko, Melinka Alonso (2020). A Bayesian binomial regression model with latent gaussian processes for modelling DNA methylation. Austrian Journal of Statistics. ISSN 1026-597X. 49(4), p. 46–56. doi: 10.17713/ajs.v49i4.1124. Full text in Research Archive Show summary

  Epigenetic observations are represented by the total number of reads from a given pool of cells and the number of methylated reads, making it reasonable to model this data by a binomial distribution. There are numerous factors that can influence the probability of success in a particular region. Moreover, there is a strong spatial (alongside the genome) dependence of these probabilities. We incorporate dependence on the covariates and the spatial dependence of the methylation probability for observations from a pool of cells by means of a binomial regression model with a latent Gaussian field and a logit link function. We apply a Bayesian approach including prior specifications on model configurations. We run a mode jumping Markov chain Monte Carlo algorithm (MJMCMC) across different choices of covariates in order to obtain the joint posterior distribution of parameters and models. This also allows finding the best set of covariates to model methylation probability within the genomic region of interest and individual marginal inclusion probabilities of the covariates.

• Mysterud, Atle; Bleka, Øyvind; Nielsen, Anders; Steinheim, Geir; Yoccoz, Nigel Gilles & Storvik, Geir Olve (2019). Climate and synchrony of lamb body mass. In Olaussen, Jon Olaf (Eds.), Contributions in natural resource economics. Festschrift to Anders Skonhoft. Fagbokforlaget. ISSN 9788245024715. p. 183–197. Full text in Research Archive
• Hubin, Aliaksandr; Storvik, Geir Olve; Grini, Paul Eivind & Butenko, Melinka Alonso (2019). Bayesian binomial regression model with a latent Gaussian field for analysis of epigenetic data. In Kharin, Y & Filzmoser, Peter (Ed.), Proceedings of Computer Data Analysis and Modeling: Stochastics and Data Science 2019. Belarusian State University Press. ISSN 9789855668115. p. 167–171. Full text in Research Archive
• Grytten, Ivar; Rand, Knut Dagestad; Nederbragt, Alexander Johan; Storvik, Geir Olve; Glad, Ingrid Kristine & Sandve, Geir Kjetil (2019). Graph Peak Caller: Calling ChIP-seq peaks on graph-based reference genomes. PLoS Computational Biology. ISSN 1553-734X. 15(2), p. 1–13. doi: 10.1371/journal.pcbi.1006731. Full text in Research Archive Show summary

  Graph-based representations are considered to be the future for reference genomes, as they allow integrated representation of the steadily increasing data on individual variation. Currently available tools allow de novo assembly of graph-based reference genomes, alignment of new read sets to the graph representation as well as certain analyses like variant calling and haplotyping. We here present a first method for calling ChIP-Seq peaks on read data aligned to a graph-based reference genome. The method is a graph generalization of the peak caller MACS2, and is implemented in an open source tool, Graph Peak Caller. By using the existing tool vg to build a pan-genome of Arabidopsis thaliana, we validate our approach by showing that Graph Peak Caller with a pan-genome reference graph can trace variants within peaks that are not part of the linear reference genome, and find peaks that in general are more motif-enriched than those found by MACS2.

• Vanem, Erik & Storvik, Geir Olve (2018). Dynamical Linear Models for Condition Monitoring with Multivariate Sensor Data. International Journal of COMADEM. ISSN 1363-7681. 21(4), p. 7–18. Full text in Research Archive
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). A Novel Algorithmic Approach to Bayesian Logic Regression. Bayesian Analysis. ISSN 1936-0975. 15(1), p. 263–311. doi: 10.1214/18-BA1141. Full text in Research Archive Show summary

  Logic regression was developed more than a decade ago as a tool to construct predictors from Boolean combinations of binary covariates. It has been mainly used to model epistatic effects in genetic association studies, which is very appealing due to the intuitive interpretation of logic expressions to describe the interaction between genetic variations. Nevertheless logic regression has (partly due to computational challenges) remained less well known than other approaches to epistatic association mapping. Here we will adapt an advanced evolutionary algorithm called GMJMCMC (Genetically modified Mode Jumping Markov Chain Monte Carlo) to perform Bayesian model selection in the space of logic regression models. After describing the algorithmic details of GMJMCMC we perform a comprehensive simulation study that illustrates its performance given logic regression terms of various complexity. Specifically GMJMCMC is shown to be able to identify three-way and even four-way interactions with relatively large power, a level of complexity which has not been achieved by previous implementations of logic regression. We apply GMJMCMC to reanalyze QTL (quantitative trait locus) mapping data for Recombinant Inbred Lines in Arabidopsis thaliana and from a backcross population in Drosophila where we identify several interesting epistatic effects. The method is implemented in an R package which is available on github.

• Hubin, Aliaksandr & Storvik, Geir Olve (2018). Mode jumping MCMC for Bayesian variable selection in GLMM. Computational Statistics & Data Analysis. ISSN 0167-9473. 127, p. 281–297. doi: 10.1016/j.csda.2018.05.020. Full text in Research Archive Show summary

  Generalized linear mixed models (GLMM) are used for inference and prediction in a wide range of different applications providing a powerful scientific tool. An increasing number of sources of data are becoming available, introducing a variety of candidate explanatory variables for these models. Selection of an optimal combination of variables is thus becoming crucial. In a Bayesian setting, the posterior distribution of the models, based on the observed data, can be viewed as a relevant measure for the model evidence. The number of possible models increases exponentially in the number of candidate variables. Moreover, the space of models has numerous local extrema in terms of posterior model probabilities. To resolve these issues a novel MCMC algorithm for the search through the model space via efficient mode jumping for GLMMs is introduced. The algorithm is based on that marginal likelihoods can be efficiently calculated within each model. It is recommended that either exact expressions or precise approximations of marginal likelihoods are applied. The suggested algorithm is applied to simulated data, the famous U.S. crime data, protein activity data and epigenetic data and is compared to several existing approaches.

• Vanem, Erik & Storvik, Geir Olve (2017). Anomaly detection using dynamical linear models and sequential testing on a marine engine system, PHM 2017 Proceedings of the Annual Conference of the Prognostics and Health Management Society 2017. PHM Society. ISSN 9781936263264. p. 185–200. Full text in Research Archive
• Rand, Knut Dagestad; Grytten, Ivar; Nederbragt, Alexander Johan; Storvik, Geir Olve; Glad, Ingrid Kristine & Sandve, Geir Kjetil (2017). Coordinates and intervals in graph-based reference genomes. BMC Bioinformatics. 18:263, p. 1–8. doi: 10.1186/s12859-017-1678-9. Full text in Research Archive Show summary

  Background It has been proposed that future reference genomes should be graph structures in order to better represent the sequence diversity present in a species. However, there is currently no standard method to represent genomic intervals, such as the positions of genes or transcription factor binding sites, on graph-based reference genomes. Results We formalize offset-based coordinate systems on graph-based reference genomes and introduce methods for representing intervals on these reference structures. We show the advantage of our methods by representing genes on a graph-based representation of the newest assembly of the human genome (GRCh38) and its alternative loci for regions that are highly variable. Conclusion More complex reference genomes, containing alternative loci, require methods to represent genomic data on these structures. Our proposed notation for genomic intervals makes it possible to fully utilize the alternative loci of the GRCh38 assembly and potential future graph-based reference genomes. We have made a Python package for representing such intervals on offset-based coordinate systems, available at https://github.com/uio-cels/offsetbasedgraph . An interactive web-tool using this Python package to visualize genes on a graph created from GRCh38 is available at https://github.com/uio-cels/genomicgraphcoords .

• Breivik, Olav Nikolai; Storvik, Geir Olve & Nedreaas, Kjell Harald (2017). Latent Gaussian models to predict historical bycatch in commercial fishery. Fisheries Research. ISSN 0165-7836. 185, p. 62–72. doi: 10.1016/j.fishres.2016.09.033. Full text in Research Archive Show summary

  Knowledge about how many fish that have been killed due to bycatch is an important aspect of ensuring a sustainable ecosystem and fishery. We introduce a Bayesian spatio-temporal prediction method for historical bycatch that incorporates two sources of available data sets, fishery data and survey data. The model used assumes that occurrence of bycatch can be described as a log-linear combination of covariates and random effects modeled as Gaussian fields. Integrated Nested Laplace Approximations (INLA) is used for fast calculations. The method introduced is general, and is applied on bycatch of juvenile cod (Gadus morhua) in the Barents Sea shrimp (Pandalus borealis) fishery. In this fishery we compare our prediction method with the well known ratio and effort methods, and make a strong case that the Bayesian spatio-temporal method produces more reliable historical bycatch predictions compared to existing methods.

• Rogers, Lauren; Storvik, Geir Olve; Knutsen, Halvor; Olsen, Esben Moland & Stenseth, Nils Christian (2017). Fine-scale population dynamics in a marine fish species inferred from dynamic state-space models. Journal of Animal Ecology. ISSN 0021-8790. 86(4), p. 888–898. doi: 10.1111/1365-2656.12678. Full text in Research Archive Show summary

  Identifying the spatial scale of population structuring is critical for the conservation of natural populations and for drawing accurate ecological inferences. However, population studies often use spatially aggregated data to draw inferences about population trends and drivers, potentially masking ecologically relevant population sub-structure and dynamics. The goals of this study were to investigate how population dynamics models with and without spatial structure affect inferences on population trends and the identification of intrinsic drivers of population dynamics (e.g. density dependence). Specifically, we developed dynamic, age-structured, state-space models to test different hypotheses regarding the spatial structure of a population complex of coastal Atlantic cod (Gadus morhua). Data were from a 93-year survey of juvenile (age 0 and 1) cod sampled along >200 km of the Norwegian Skagerrak coast. We compared two models: one which assumes all sampled cod belong to one larger population, and a second which assumes that each fjord contains a unique population with locally determined dynamics. Using the best supported model, we then reconstructed the historical spatial and temporal dynamics of Skagerrak coastal cod. Cross-validation showed that the spatially structured model with local dynamics had better predictive ability. Furthermore, posterior predictive checks showed that a model which assumes one homogeneous population failed to capture the spatial correlation pattern present in the survey data. The spatially structured model indicated that population trends differed markedly among fjords, as did estimates of population parameters including density-dependent survival. Recent biomass was estimated to be at a near-record low all along the coast, but the finer scale model indicated that the decline occurred at different times in different regions. Warm temperatures were associated with poor recruitment, but local changes in habitat and fishing pressure may have played a role in driving local dynamics. More generally, we demonstrated how state-space models can be used to test evidence for population spatial structure based on survey time-series data. Our study shows the importance of considering spatially structured dynamics, as the inferences from such an approach can lead to a different ecological understanding of the drivers of population declines, and fundamentally different management actions to restore populations.

• Hubin, Aliaksandr & Storvik, Geir Olve (2016). On Mode Jumping in MCMC for Bayesian Variable Selection within GLMM. In Aivazian, S; Filzmoser, Peter & Kharin, Y (Ed.), COMPUTER DATA ANALYSIS AND MODELING. Theoretical and Applied Stochastics. Proceedings of the XI International Conference.. Belarusian State University. ISSN 9789855533666. p. 275–278. doi: 10.1016/j.csda.2018.05.020. Full text in Research Archive Show summary

  Generalized linear mixed models (GLMM) are addressed for inference and prediction in a wide range of different applications providing a powerful scientific tool for the researchers and analysts coming from different fields. At the same time more sources of data are becoming available introducing a variety of hypothetical explanatory variables for these models to be considered. Estimation of posterior model probabilities and selection of an optimal model is thus becoming crucial. We suggest a novel mode jumping MCMC procedure for Bayesian model averaging and model selection in GLMM.

• Breivik, Olav Nikolai; Storvik, Geir Olve & Nedreaas, Kjell Harald (2016). Latent gaussian models to decide on spatial closures for bycatch management in the barents sea shrimp fishery. Canadian Journal of Fisheries and Aquatic Sciences. ISSN 0706-652X. 73(8), p. 1271–1280. doi: 10.1139/cjfas-2015-0322. Full text in Research Archive Show summary

  In the Barents Sea and adjacent water, fishing grounds are closed for shrimp fishing by the Norwegian Directorate of Fisheries Monitoring and Surveillance Service (MSS) if the expected number of juvenile fish caught are predicted to exceed a certain limit per kilogram shrimp (Pandalus borealis). Today, a simple ratio estimator, which does not fully utilize all data available, is in use. In this paper, we construct a Bayesian hierarchical spatiotemporal model for improved prediction of the bycatch ratio in the Barents Sea shrimp fishery. More predictable bycatch will be an advantage for the MSS because of more correct decisions and better resource allocation and also for the fishermen because of more predictable fishing grounds. The model assumes that the occurrence of shrimp and juvenile Atlantic cod (Gadus morhua) can be modeled by linked regression models containing several covariates (including 0-group abundance estimates) and random effects modeled as Gaussian fields. Integrated nested Laplace approximations is applied for fast calculation. The method is applied to prediction of the bycatch ratio for Atlantic cod.

• Bleka, Øyvind; Benschop, Corina C G; Storvik, Geir Olve & Gill, Peter (2016). A comparative study of qualitative and quantitative models used to interpret complex STR DNA profiles. Forensic Science International: Genetics. ISSN 1872-4973. 25, p. 85–96. doi: 10.1016/j.fsigen.2016.07.016. Full text in Research Archive Show summary

  The investigation of the performance of models to interpret complex DNA profiles is best undertaken using real DNA profiles. Here we used a data set to reflect the variety typically encountered in real casework. The “crime-stains” were constructed from known individuals and comprised a total of 59 diverse samples: pristine DNA/DNA extracted from blood, 2–3 person mixtures, degradation/no-degradation, differences in allele sharing, dropout/no dropout, etc. Two siblings were also included in the test-set in order to challenge the systems. Two kinds of analyses were performed, namely tests on whether a person of interest is a contributor based on weight-of-evidence (likelihood ratio) calculations, and deconvolution test to estimate the profile of unknown constituent parts. The weight-of-evidence analyses compared LRmix Studio with EuroForMix including exploration of the effect of applying an ad hoc stutter-filter. For the deconvolution analysis we compared EuroForMix with LoCIM-tool. When we classified persons of interests into being true contributors or non-contributors, we found that EuroForMix, overall, returned a higher true positive rate for the same false positive levels compared to LRmix. In particular, in cases with an unknown major component, EuroForMix was more discriminating for mixtures where the person of interest was a minor contributor. Comparing deconvolution of major contributors we found that EuroForMix overall performed better than LoCIM-tool.

• Bleka, Øyvind; Storvik, Geir Olve & Gill, Peter (2016). EuroForMix: An open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts. Forensic Science International: Genetics. ISSN 1872-4973. 21, p. 35–44. doi: 10.1016/j.fsigen.2015.11.008. Full text in Research Archive Show summary

  We have released a software named EuroForMix to analyze STR DNA profiles in a user-friendly graphical user interface. The software implements a model to explain the allelic peak height on a continuous scale in order to carry out weight-of-evidence calculations for profiles which could be from a mixture of contributors. Through a properly parameterized model we are able to do inference on mixture proportions, the peak height properties, stutter proportion and degradation. In addition, EuroForMix includes models for allele drop-out, allele drop-in and sub-population structure. EuroForMix supports two inference approaches for likelihood ratio calculations. The first approach uses maximum likelihood estimation of the unknown parameters. The second approach is Bayesian based which requires prior distributions to be specified for the parameters involved. The user may specify any number of known and unknown contributors in the model, however we find that there is a practical computing time limit which restricts the model to a maximum of four unknown contributors. EuroForMix is the first freely open source, continuous model (accommodating peak height, stutter, drop-in, drop-out, population substructure and degradation), to be reported in the literature. It therefore serves an important purpose to act as an unrestricted platform to compare different solutions that are available. The implementation of the continuous model used in the software showed close to identical results to the R-package DNAmixtures, which requires a HUGIN Expert license to be used. An additional feature in EuroForMix is the ability for the user to adapt the Bayesian inference framework by incorporating their own prior information.

• Eikeset, Anne Maria; Dunlop, Erin; Heino, Mikko Petteri; Storvik, Geir Olve; Stenseth, Nils Christian & Dieckmann, Ulf (2016). Roles of density-dependent growth and life history evolution in accounting for fisheries-induced trait changes. Proceedings of the National Academy of Sciences of the United States of America (PNAS). ISSN 0027-8424. 113(52), p. 15030–15035. doi: 10.1073/pnas.1525749113. Full text in Research Archive
• Heier, Lise; Viljugrein, Hildegunn & Storvik, Geir Olve (2015). Persistence of plague outbreaks among great gerbils in Kazakhstan: effects of host population dynamics. Population Ecology. ISSN 1438-3896. 57(3), p. 473–484. doi: 10.1007/s10144-015-0500-7. Full text in Research Archive
• Marques, Reinaldo A. Gomes & Storvik, Geir Olve (2014). Reweighting Schemes Based on Particle Methods. In Lanzarone, Ettore & Ieva, Francesca (Ed.), The Contribution of Young Researchers to Bayesian Statistics. Springer. ISSN 9783319020846. p. 73–76. doi: 10.1007/978-3-319-02084-6_14. Full text in Research Archive Show summary

  Sequential Monte Carlo methods are widely used to deal with the intractability of complex models including state space models. Their aim is to approximate the distribution of interest by a set of properly weighted samples. To control the weight degeneracy, the resample step has been proposed as an inexpensive alternative to avoid the collapse of particle filter algorithms. When the sample becomes too poor with successive use of resample steps, MCMC moves have been added in particle filter algorithms in order to make the identical samples diverge. In this work we consider strategies where we first perform a moves step, and then we update the weights for reweighting the particles. The validity of this approach is based on the commonly used trick of working on an artificial extended distribution having the target distribution as marginal combined with the use of backwards kernels. By updating the weights via a diversification step, this approach can make their empirical distribution less skewed increasing the effective sample size.

• Rivrud, Inger Maren; Sonkoly, Krisztina; Lehoczki, Róbert; Csányi, Sándor; Storvik, Geir Olve & Mysterud, Atle (2013). Hunter selection and long-term trend (1881-2008) of red deer trophy sizes in Hungary. Journal of Applied Ecology. ISSN 0021-8901. 50(1), p. 168–180. doi: 10.1111/1365-2664.12004. Full text in Research Archive
• Storvik, Geir Olve (2012). Bootstrapping. In Veierød, Marit Bragelien; Lydersen, Stian & Laake, Petter (Ed.), Medical statistics in clinical and epidemiological research. Gyldendal Akademisk. ISSN 9788205399594. p. 402–428. Full text in Research Archive
• Storvik, Geir Olve (2012). Bayesian methods. In Veierød, Marit Bragelien; Lydersen, Stian & Laake, Petter (Ed.), Medical statistics in clinical and epidemiological research. Gyldendal Akademisk. ISSN 9788205399594. p. 559–584. Full text in Research Archive
• Hirst, David; Storvik, Geir Olve; Rognebakke, Hanne; Aldrin, Magne; Aanes, Sondre & Vølstad, Jon Helge (2012). A Bayesian modelling framework for the estimation of catch-at-age of commercially harvested fish species. Canadian journal of fisheries and aquatic sciences. Supplem ent = Journ al canadien des sciences halieutiques et aquati qu. ISSN 0714-7937. 69(12), p. 2064–2076. doi: 10.1139/CJFAS-2012-0075. Full text in Research Archive
• Nielsen, Anders; Yoccoz, Nigel; Steinheim, Geir; Storvik, Geir Olve; Rekdal, Yngve & Angeloff, Michael [Show all 9 contributors for this article] (2012). Are responses of herbivores to environmental variability spatially consistent in alpine ecosystems? Global Change Biology. ISSN 1354-1013. 18(10), p. 3050–3062. doi: 10.1111/j.1365-2486.2012.02733.x. Full text in Research Archive

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• Palomares, Alfonso Diz-Lois & Storvik, Geir Olve (2026). Parameter estimation in Conditional Sequential Monte Carlo algorithms through Particle Learning. Full text in Research Archive Show summary

  In this work, we explore particle learning strategies for the joint estimation of static parameters and latent states within conditional sequential Monte Carlo (CSMC) algorithms. Building on this idea, we propose the p(parameter)-CSMC algorithm, which incorporates both parameter learning and ancestor sampling, leading to much better mixing properties compared to Gibbs sampling in settings where strong internal correlations may challenge effective exploration. We include an application to the estimation of weights in a branching process model against synthetic data and show that, in this setting, performance is dramatically enhanced, with substantially faster mixing and markedly reduced autocorrelation compared with standard particle Gibbs implementations.

• Storvik, Geir Olve (2025). Maskinlæring versus statistisk modellering. Full text in Research Archive
• Storvik, Geir Olve (2025). Maskinlæring versus statistisk modellering. Full text in Research Archive
• Storvik, Geir Olve; Engebretsen, Solveig; Blasio, Birgitte Freiesleben De & Frigessi, Arnoldo (2025). Flaws in the Article “Nearly Instantaneous Time-Varying Reproduction Number for Contagious Diseases—a Direct Approach Based on Nonlinear Regression". Journal of Computational Biology. ISSN 1066-5277. 32(8), p. 813–818. doi: https:/www.liebertpub.com/doi/full/10.1177/15578666251360613. Full text in Research Archive
• Engebretsen, Solveig; Thorarinsdottir, Thordis Linda; Palomares, Alfonso Diz-Lois; Storvik, Geir Olve; Frigessi, Arnoldo & Blasio, Birgitte Freiesleben De (2025). Contribution to the Discussion of 'Some statistical aspects of the Covid-19 response' by Wood et al. Journal of the Royal Statistical Society, Series A (Statistics in Society). ISSN 0964-1998. 189(1), p. 71–72. doi: 10.1093/jrsssa/qnaf080. Full text in Research Archive
• Hubin, Aliaksandr; Storvik, Geir Olve; Frommlet, Florian & Lachmann, Jon (2024). A subsampling MCMC approach for Bayesian model selection and model averaging. Full text in Research Archive
• Storvik, Geir Olve & Palomares, Alfonso Diz-Lois (2023). Sequential Monte Carlo for infectious disease models - the Covid-19 case. Full text in Research Archive
• Lachmann, Jon; Storvik, Geir Olve; Frommlet, Florian & Hubin, Aliaksandr (2022). A subsampling approach for Bayesian model selection. Full text in Research Archive
• Hubin, Aliaksandr & Storvik, Geir Olve (2022). Variational Bayes for inference on model and parameter uncertainty in Bayesian neural networks. Full text in Research Archive Show summary

  Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques [1]. There are several advantages of using a Bayesian approach: Parameter and prediction uncertainties become easily available, facilitating rigorous statistical analysis. Furthermore, prior knowledge can be incorporated. However, so far there have been no scalable techniques capable of combining both model (structural) and parameter uncertainty. In the presented piece of research [2] we introduce the concept of model uncertainty in BNNs and hence make inference in the joint space of models and parameters. Moreover, we suggest an adaptation of a scalable variational inference approach with reparametrization of marginal inclusion probabilities to incorporate the model space constraints. Experimental results on a range of benchmark data sets show that we obtain comparable accuracy results with the competing models, but based on methods that are much more sparse than ordinary BNNs. This is particularly the case in model selection settings, but also within a Bayesian model averaging setting a considerable sparsification is achieved. As expected, model uncertainties give higher, but more reliable uncertainty measures. References: [1] Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D. (2015). Weight uncertainty in neural networks. International Conference on Machine Learning, 1613 - 1622. [2] Hubin, A., Storvik, G. (2019). Combining model and parameter uncertainty in Bayesian neural networks, arXiv preprint arXiv:1903.07594.

• Hubin, Aliaksandr & Storvik, Geir Olve (2022). Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty. Full text in Research Archive
• Hubin, Aliaksandr & Storvik, Geir Olve (2021). Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty. Full text in Research Archive Show summary

  Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using a Bayesian approach: Parameter and prediction uncertainties become easily available, facilitating rigorous statistical analysis. Furthermore, prior knowledge can be incorporated. However so far there have been no scalable techniques capable of combining both model (structural) and parameter uncertainty. In this paper we introduce the concept of model uncertainty in BNNs and hence make inference in the joint space of models and parameters. Moreover, we suggest an adaptation of a scalable variational inference approach with reparametrization of marginal inclusion probabilities to incorporate the model space constraints. Experimental results on a range of benchmark data sets show that we obtain comparable accuracy results with the competing models, but based on methods that are much more sparse than ordinary BNNs. This is particularly the case in model selection settings, but also within a Bayesian model averaging setting a considerable sparsification is achieved. As expected, model uncertainties give higher, but more reliable uncertainty measures.

• Hubin, Aliaksandr; Frommlet, Florian & Storvik, Geir Olve (2021). Reversible Genetically Modified MCMCs. Full text in Research Archive Show summary

  In this work, we introduce a reversible version of a genetically modified Markov chain Monte Carlo algorithm (GMJMCMC) for inference on posterior model probabilities in complex functional spaces, where the number of explanatory variables or functions of explanatory variables is prohibitively large for simple Markov Chain Monte Carlo methods. A genetically modified Markov chain Monte Carlo algorithm (GMJMCMC) was introduced in [5, 4, 2] for Bayesian model selection/averaging problems when the total number of function of covariates is prohibitively large. More specifically, these applications include GWAS studies with Bayesian generalized linear models [2] as well as Bayesian logic regressions [5] and Bayesian generalized nonlinear models [4]. If its regularity conditions are met, GMJMCMC algorithm can asymptotically explore all models in the defined model spaces. At the same time, GMJMCMC is not a proper MCMC in a sense that its limiting distribution does not correspond to marginal posterior model probabilities and thus only renormalized estimates of these probabilities [3, 1] can be obtained. Unlike the standard GMJMCMC algorithm, the introduced algorithm is a proper MCMC and its limiting distribution corresponds to posterior marginal model probabilities in the explored model spaces under reasonable regularity conditions.

• Hubin, Aliaksandr & Storvik, Geir Olve (2021). Variational Bayes for inference on model and parameter uncertainty in Bayesian neural networks. Full text in Research Archive Show summary

  Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques [1]. There are several advantages of using a Bayesian approach: Parameter and prediction uncertainties become easily available, facilitating rigorous statistical analysis. Furthermore, prior knowledge can be incorporated. However so far there have been no scalable techniques capable of combining both model (structural) and parameter uncertainty. In the presented piece of research [2] we introduce the concept of model uncertainty in BNNs and hence make inference in the joint space of models and parameters. Moreover, we suggest an adaptation of a scalable variational inference approach with reparametrization of marginal inclusion probabilities to incorporate the model space constraints. Experimental results on a range of benchmark data sets show that we obtain comparable accuracy results with the competing models, but based on methods that are much more sparse than ordinary BNNs. This is particularly the case in model selection settings, but also within a Bayesian model averaging setting a considerable sparsification is achieved. As expected, model uncertainties give higher, but more reliable uncertainty measures.

• Storvik, Geir Olve (2021). Bayesian Approaches to Neural Networks. Full text in Research Archive
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2020). A novel algorithmic approach to Bayesian Logic Regression. Full text in Research Archive Show summary

  Logic regression was developed more than a decade ago as a tool to construct predictors from Boolean combinations of binary covariates. It has been mainly used to model epistatic effects in genetic association studies, which is very appealing due to the intuitive interpretation of logic expressions to describe the interaction between genetic variations. Nevertheless logic regression has (partly due to computational challenges) remained less well known than other approaches to epistatic association mapping. Here we will adapt an advanced evolutionary algorithm called GMJMCMC (Genetically modified Mode Jumping Markov Chain Monte Carlo) to perform Bayesian model selection in the space of logic regression models. After describing the algorithmic details of GMJMCMC we perform a comprehensive simulation study that illustrates its performance given logic regression terms of various complexity. Specifically GMJMCMC is shown to be able to identify three-way and even four-way interactions with relatively large power, a level of complexity which has not been achieved by previous implementations of logic regression. We apply GMJMCMC to reanalyze QTL (quantitative trait locus) mapping data for Recombinant Inbred Lines in Arabidopsis thaliana and from a backcross population in Drosophila where we identify several interesting epistatic effects. The method is implemented in an R package which is available on github.

• Storvik, Geir Olve (2020). Neural networks vs generalized linear models. Full text in Research Archive
• Storvik, Geir Olve (2020). "Preliminaries for Deep Neural Networks: Recapture of Linear Algebra, Gradient Descents and Generalized Linear Models". Full text in Research Archive
• Hubin, Aliaksandr & Storvik, Geir Olve (2019). Combining Model and Parameter Uncertainty in Bayesian Neural Networks. Full text in Research Archive Show summary

  Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using Bayesian approach: Parameter and prediction uncertainty become easily available, facilitating rigid statistical analysis. Furthermore, prior knowledge can be incorporated. However so far there have been no scalable techniques capable of combining both model (structural) and parameter uncertainty. In this paper we introduce the concept of model uncertainty in BNNs and hence make inference in the joint space of models and parameters. Moreover, we suggest an adaptation of a scalable variational inference approach with reparametrization of marginal inclusion probabilities to incorporate the model space constraints. Finally, we show that incorporating model uncertainty via Bayesian model averaging and Bayesian model selection allows to drastically sparsify the structure of BNNs without significant loss of predictive power.

• Hubin, Aliaksandr & Storvik, Geir Olve (2019). Combining Model and Parameter Uncertainty in Bayesian Neural Networks. Full text in Research Archive
• Storvik, Geir Olve (2019). Bayesian approaches to neural networks. Full text in Research Archive
• Storvik, Geir Olve (2019). On the use of Bayesian methods in machine learning. Full text in Research Archive
• Storvik, Geir Olve (2019). Bayesian approaches to neural networks. Full text in Research Archive
• Storvik, Geir Olve & Hubin, Aliaksandr (2019). Combining model and parameter uncertainty in Bayesian neural networks. Full text in Research Archive
• Storvik, Geir Olve (2019). Flexible Bayesian Nonlinear Model Configuration. Full text in Research Archive
• Hubin, Aliaksandr & Storvik, Geir Olve (2019). Combining Model and Parameter Uncertainty in Bayesian Neural Networks. Full text in Research Archive Show summary

  Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using Bayesian approach: Parameter and prediction uncertainty become easily available, facilitating rigid statistical analysis. Furthermore, prior knowledge can be incorporated. However so far there have been no scalable techniques capable of combining both model (structural) and parameter uncertainty. In this paper we introduce the concept of model uncertainty in BNNs and hence make inference in the joint space of models and parameters. Moreover, we suggest an adaptation of a scalable variational inference approach with reparametrization of marginal inclusion probabilities to incorporate the model space constraints. Finally, we show that incorporating model uncertainty via Bayesian model averaging and Bayesian model selection allows to drastically sparsify the structure of BNNs without significant loss of predictive power.

• Hubin, Aliaksandr & Storvik, Geir Olve (2019). Combining Model and Parameter Uncertainty in Bayesian Neural Networks. Full text in Research Archive
• Hubin, Aliaksandr; Storvik, Geir Olve; Grini, Paul Eivind & Butenko, Melinka Alonso (2019). Bayesian binomial regression model with a latent Gaussian field for analysis of epigenetic data. Full text in Research Archive
• Hubin, Aliaksandr & Storvik, Geir Olve (2019). Combining Model and Parameter Uncertainty in Bayesian Neural Networks. Full text in Research Archive
• Storvik, Geir Olve (2019). Education in Statistics and Data Science at the University of Oslo. Full text in Research Archive
• Storvik, Geir Olve (2019). Bayesian approaches to neural networks. Full text in Research Archive
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models. Full text in Research Archive
• Storvik, Geir Olve (2018). Statistical analysis for Time series. Full text in Research Archive
• Storvik, Geir Olve (2018). Preliminaries for Deep Neural Networks: Recapture of Linear Algebra, Gradient Descents and Generalized Linear Models. Full text in Research Archive
• Storvik, Geir Olve; Vigeland, Magnus Dehli; Caliebe, Amke & Egeland, Thore (2018). Specification of mutation probabilities through Metropolis-Hastings steps. Full text in Research Archive
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models. Full text in Research Archive Show summary

  Regression models are used for inference and prediction in a wide range of applications providing a powerful scientific tool for researchers and analysts from different fields. In many research fields the amount of available data as well as the number of potential explanatory variables is rapidly increasing. Variable selection and model averaging have become extremely important tools for improving inference and prediction. However, often linear models are not sufficient and the complex relationship between input variables and a response is better described by introducing non-linearities and complex functional interactions. Deep learning models have been extremely successful in terms of prediction although they are often difficult to specify and potentially suffer from overfitting. The aim of this paper is to bring the ideas of deep learning into a statistical framework which yields more parsimonious models and allows to quantify model uncertainty. To this end we introduce the class of deep Bayesian regression models (DBRM) consisting of a generalized linear model combined with a comprehensive non-linear feature space, where non-linear features are generated just like in deep learning but combined with variable selection in order to include only important features. DBRM can easily be extended to include latent Gaussian variables to model complex correlation structures between observations, which seems to be not easily possible with existing deep learning approaches. Two different algorithms based on MCMC are introduced to fit DBRM and to perform Bayesian inference. The predictive performance of these algorithms is compared with a large number of state of the art algorithms. Furthermore we illustrate how DBRM can be used for model inference in various applications.

• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models. Full text in Research Archive
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models. Full text in Research Archive Show summary

  Regression models are used for inference and prediction in a wide range of applications providing a powerful scientific tool for researchers and analysts from different fields. In many research fields the amount of available data as well as the number of potential explanatory variables is rapidly increasing. Variable selection and model averaging have become extremely important tools for improving inference and prediction. However, often linear models are not sufficient and the complex relationship between input variables and a response is better described by introducing non-linearities and complex functional interactions. Deep learning models have been extremely successful in terms of prediction although they are often difficult to specify and potentially suffer from overfitting. The aim of this paper is to bring the ideas of deep learning into a statistical framework which yields more parsimonious models and allows to quantify model uncertainty. To this end we introduce the class of deep Bayesian regression models (DBRM) consisting of a generalized linear model combined with a comprehensive non-linear feature space, where non-linear features are generated just like in deep learning but combined with variable selection in order to include only important features. DBRM can easily be extended to include latent Gaussian variables to model complex correlation structures between observations, which seems to be not easily possible with existing deep learning approaches. Two different algorithms based on MCMC are introduced to fit DBRM and to perform Bayesian inference. The predictive performance of these algorithms is compared with a large number of state of the art algorithms. Furthermore we illustrate how DBRM can be used for model inference in various applications.

• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models. Full text in Research Archive Show summary

  Regression models are used for inference and prediction in a wide range of applications providing a powerful scientific tool for researchers and analysts from different fields. In many research fields the amount of available data as well as the number of potential explanatory variables is rapidly increasing. Variable selection and model averaging have become extremely important tools for improving inference and prediction. However, often linear models are not sufficient and the complex relationship between input variables and a response is better described by introducing non-linearities and complex functional interactions. Deep learning models have been extremely successful in terms of prediction although they are often difficult to specify and potentially suffer from overfitting. The aim of this paper is to bring the ideas of deep learning into a statistical framework which yields more parsimonious models and allows to quantify model uncertainty. To this end we introduce the class of deep Bayesian regression models (DBRM) consisting of a generalized linear model combined with a comprehensive non-linear feature space, where non-linear features are generated just like in deep learning but combined with variable selection in order to include only important features. DBRM can easily be extended to include latent Gaussian variables to model complex correlation structures between observations, which seems to be not easily possible with existing deep learning approaches. Two different algorithms based on MCMC are introduced to fit DBRM and to perform Bayesian inference. The predictive performance of these algorithms is compared with a large number of state of the art algorithms. Furthermore we illustrate how DBRM can be used for model inference in various applications.

• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models. Full text in Research Archive
• Eikeset, Anne Maria; Dunlop, Erin; Heino, Mikko Petteri; Storvik, Geir Olve; Stenseth, Nils Christian & Dieckmann, Ulf (2017). Reply to Enberg and Jørgensen: Ecology and evolution both matter for explaining stock dynamics. Proceedings of the National Academy of Sciences of the United States of America (PNAS). ISSN 0027-8424. 114(22), p. E4322–E4323. doi: 10.1073/pnas.1703865114. Full text in Research Archive
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2017). A novel algorithmic approach to Bayesian Logic Regression. Full text in Research Archive
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2017). Deep non-linear regression models in a Bayesian framework. Full text in Research Archive
• Hubin, Aliaksandr; Storvik, Geir Olve & Grini, Paul Eivind (2017). Variable selection in binomial regression with latent Gaussian field models for analysis of epigenetic data. Full text in Research Archive
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2017). A novel algorithmic approach to Bayesian Logic Regression. Full text in Research Archive
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2017). A novel GMJMCMC algorithm for Bayesian Logic Regression models. Full text in Research Archive
• Hubin, Aliaksandr & Storvik, Geir Olve (2017). Efficient mode jumping MCMC for Bayesian variable selection and model averaging in GLMM. Full text in Research Archive
• Hubin, Aliaksandr & Storvik, Geir Olve (2016). Variable selection in logistic regression with a latent Gaussian field models with an application in epigenomics. Full text in Research Archive
• Hubin, Aliaksandr & Storvik, Geir Olve (2016). VARIABLE SELECTION IN BINOMIAL REGRESSION WITH LATENT GAUSSIAN FIELD MODELS FOR ANALYSIS OF EPIGENETIC DATA. Full text in Research Archive
• Hubin, Aliaksandr & Storvik, Geir Olve (2016). Efficient mode jumping MCMC for Bayesian variable selection in GLM with random effects models. Full text in Research Archive
• Hubin, Aliaksandr & Storvik, Geir Olve (2016). On Mode Jumping in MCMC for Bayesian Variable Selection within GLMM. Full text in Research Archive
• Vanem, Erik; Glad, Ingrid Kristine & Storvik, Geir Olve (2016). Dynamical Linear Models for Condition Monitoring with Multivariate Sensor Data. Full text in Research Archive
• Hubin, Aliaksandr & Storvik, Geir Olve (2015). Variable selection in binomial regression with a latent Gaussian field models for analysis of epigenetic data. Full text in Research Archive
• Hubin, Aliaksandr & Storvik, Geir Olve (2015). On model selection in Hidden Markov Models with covariates. Full text in Research Archive
• Hubin, Aliaksandr & Storvik, Geir Olve (2015). Variable selection in binomial regression with a latent Gaussian field models for analysis of epigenetic data. Full text in Research Archive
• Storvik, Geir Olve; Aanes, Sondre & Maisha, Peter Nyangweso (2015). Estimation of fish abundance and demography in the Barents sea. Full text in Research Archive
• Storvik, Geir Olve (2015). Estimation of fish abundance and demography in the Barents sea. Full text in Research Archive
• Storvik, Geir Olve & Marques, Reinaldo (2014). Estimation of static parameters using particle filters and a block independence approximation. Full text in Research Archive
• Otero, Jaime; L'Abee-Lund, Jan Henning; Castro-Santos, Ted; Leonardsson, Kjell; Storvik, Geir Olve & Jonsson, Bror [Show all 46 contributors for this article] (2014). Basin-scale phenology and effects of climate variability on global timing of initial seaward migration of Atlantic salmon (Salmo salar). Global Change Biology. doi: 10.1111/gcb.12363. Full text in Research Archive Show summary

  Migrations between different habitats are key events in the lives of many organisms. Such movements involve annually recurring travel over long distances usually triggered by seasonal changes in the environment. Often, the migration is associated with travel to or from reproduction areas to regions of growth. Young anadromous Atlantic salmon (Salmo salar) emigrate from freshwater nursery areas during spring and early summer to feed and grow in the North Atlantic Ocean. The transition from the freshwater (‘parr’) stage to the migratory stage where they descend streams and enter salt water (‘smolt’) is characterized by morphological, physiological and behavioural changes where the timing of this parr -smolt transition is cued by photoperiod and water temperature. Environmental conditions in the freshwater habitat control the downstream migration and contribute to within - and among - river variation in migratory timing. Moreover, the timing of the freshwater emigration has likely evolved to meet environmental conditions in the ocean as these affect growth and survival of the post-smolts. Using generalized additive mixed -effects modelling, we analysed spatio-temporal variations in the dates of downstream smolt migration in 67 rivers throughout the North Atlantic during the last five decades and found that migrations were earlier in populations in the east than the west. After accounting for this spatial effect, the initiation of the downstream migration among rivers was positively associated with freshwater temperatures, up to about 10 ºC and levelling off at higher values, and with sea-surface temperatures. Earlier migration occurred when river discharge levels were low but increasing. On average, the initiation of the smolt seaward migration has occurred 2.5 days earlier per decade throughout the basin of the North Atlantic. This shift in phenology matches changes in air, river, and ocean temperatures, suggesting that Atlantic salmon emigration is responding to the current global climate changes.

• Storvik, Geir Olve (2013). Computational tools for analysis of models with latent structures. Full text in Research Archive
• Storvik, Geir Olve (2012). Bayesian approaches to neural networks. Full text in Research Archive
• Sunde, Leif-Martin Sæther; Hubin, Aliaksandr & Storvik, Geir Olve (2023). Spherical Priors for Bayesian Deep Learning. Universitetet i Oslo. Full text in Research Archive
• Storvik, Geir Olve & Hubin, A. (2018). Bayesian model configuration, selection and averaging in complex regression contexts. Universitetet i Oslo. Full text in Research Archive
• Storvik, Geir Olve & Marques, Reinaldo (2018). On Monte Carlo Contributions for Real-time Probabilistic Inference. Universitetet i Oslo. Full text in Research Archive
• Rognebakke, Hanne; Hirst, David; Aanes, Sondre & Storvik, Geir Olve (2016). Catch-at-age - Version 4.0: Technical Report. Norsk Regnesentral. Full text in Research Archive Show summary

  The Norwegian Computing Center and the Institute of Marine Research have over years developed a Bayesian hierarchical model to estimate the catch-at-age of fish. Such a model enables us to obtain estimates of the catch-at-age with appropriate uncertainty. This is considered as essential input in most age structured stock assessment processes. Recent improvements of the model include modelling a haulsize effect in the proportionat-age model. The model is implemented in C with an R interface. This note gives a thorough description of the model and the simulation algorithm corresponding to version 4.0 of the program.

• Storvik, Geir Olve; Løland, Anders; Lykkja, Ola Martin & Gjevestad, Jon Glenn Omholt (2016). SAVE – tracking vehicle movements for toll object detection using particle filter. Norsk Regnesentral. Full text in Research Archive
• Maisha, Peter Nyangweso; Storvik, Geir Olve & Aanes, Sondre (2015). A State-Space Model for Abundance Estimation from Bottom Trawl Data with Applications to Norwegian Winter Survey. Matematisk Institutt, UiO. Full text in Research Archive Show summary

  We study a hierarchical dynamic state-space model for abundance estimation. A generic data fusion approach for combining computer simulated posterior samples of catch output data with observed research survey indices using sequential importance sampling is presented. Posterior samples of catch generated from a computer software are used as a primary source of input data through which fisheries dependent information is mediated. Direct total stock abundance estimates are obtained without the need to estimate any intermediate parameters such as catchability and mortality. Numerical results of a simulation study show that our method provides a useful alternative to existing methods. We apply the method to data from the Barents Sea Winter survey for Northeast Arctic cod (Gadus morhua). The results based on our method are comparable to results based on current methods.

• Marques, Reinaldo A. Gomes & Storvik, Geir Olve (2013). Particle move-reweighting strategies for online inference. Universitetet i Oslo. Full text in Research Archive Show summary

  Sequential Monte Carlo (SMC) methods are one of the most important computational tool to deal with intractability in complex statistical models. In those techniques, the distribution of interest is approximated by a set of properly weighted samples. One problem with SMC algorithms is the weights degeneracy: either the weights have huge variability or high correlations between the particles. Updating the particles by a few MCMC steps has been suggested as an improvement in this case (the resample-move algorithm). The general setup is to first resample the particles in such a way that all particles are given equal weight. Thereafter the MCMC steps are applied in order to make the identical samples diverge. In this work we consider an alternative strategy where the order of MCMC updates and the resampling steps are switched, i.e. MCMC updates are performed first. The main advantage with such an approach is that by performing MCMC updates, the weights can be updated simultaneously, making them less variable. We illustrate through simulation studies how our methodology can give improved results for online Bayesian inference in general state space models.

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