Geir Olve Storvik

• 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), s 1- 13 . doi: 10.1371/journal.pcbi.1006731
• Hubin, Aliaksandr & Storvik, Geir Olve (2018). Mode jumping MCMC for Bayesian variable selection in GLMM. Computational Statistics & Data Analysis.  ISSN 0167-9473.  127, s 281- 297 . doi: 10.1016/j.csda.2018.05.020 Fulltekst i vitenarkiv.
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). A Novel Algorithmic Approach to Bayesian Logic Regression. Bayesian Analysis.  ISSN 1936-0975. . doi: 10.1214/18-BA1141 Vis sammendrag

  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.

• 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), s 7- 18
• 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, s 62- 72 . doi: 10.1016/j.fishres.2016.09.033 Fulltekst i vitenarkiv. Vis sammendrag

  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.

• 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.  ISSN 1471-2105.  18:263, s 1- 8 . doi: 10.1186/s12859-017-1678-9 Fulltekst i vitenarkiv.
• 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), s 888- 898 . doi: 10.1111/1365-2656.12678 Fulltekst i vitenarkiv.
• Vanem, Erik & Storvik, Geir Olve (2017). Anomaly Detection Using Dynamical Linear Models and Sequential Testing on a Marine Engine System, In  PHM 2017 Proceedings of the Annual Conference of the Prognostics and Health Management Society 2017.  PHM Society.  ISBN 978-1-936263-26-4.  -.  s 185 - 200
• 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, s 85- 96 . doi: 10.1016/j.fsigen.2016.07.016 Fulltekst i vitenarkiv.
• 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, s 35- 44 . doi: 10.1016/j.fsigen.2015.11.008 Fulltekst i vitenarkiv.
• 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), s 1271- 1280 . doi: 10.1139/cjfas-2015-0322 Fulltekst i vitenarkiv.
• 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.  ISSN 0027-8424.  113(52), s 15030- 15035 . doi: 10.1073/pnas.1525749113
• Hubin, Aliaksandr & Storvik, Geir Olve (2016). On Mode Jumping in MCMC for Bayesian Variable Selection within GLMM, In S Aivazian; Peter Filzmoser & Y Kharin (ed.),  COMPUTER DATA ANALYSIS AND MODELING. Theoretical and Applied Stochastics. Proceedings of the XI International Conference..  Belarusian State University.  ISBN 978-985-553-366-6.  KAPITTEL.  s 275 - 278 Vis sammendrag

  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.

• 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), s 473- 484 . doi: 10.1007/s10144-015-0500-7
• Gomes Marques, Reinaldo A. & Storvik, Geir Olve (2014). Reweighting Schemes Based on Particle Methods, In Ettore Lanzarone & Francesca Ieva (ed.),  The Contribution of Young Researchers to Bayesian Statistics.  Springer.  ISBN 978-3-319-02084-6.  14.  s 73 - 76 Vis sammendrag

  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.

• Otero, Jaime; L'Abee-Lund, Jan Henning; Castro-Santos, Ted; Leonardsson, Kjell; Storvik, Geir Olve; Jonsson, Bror; Dempson, J. Brian; Russell, Ian C.; Jensen, Arne Johan; Baglinière, Jean-Luc; Dionne, Mélanie; Armstrong, John D.; Romakkaniemi, Atso; Letcher, Benjamin H.; Kocik, John F.; Erkinaro, Jaakko; Poole, Russell; Rogan, Ger; Lundqvist, Hans; MacLean, Julian C.; Jokikokko, Erkki; Arnekleiv, Jo Vegar; Kennedy, Richard J.; Niemelä, Eero; Caballero, Pablo; Music, Paul A.; Antonsson, Thorolfur; Gudjonsson, Sigurdur; Veselov, Alexey E.; Lamberg, Anders; Groom, Steve; Taylor, Benjamin H.; Taberner, Malcolm; Dillane, Mary; Arnason, Fridthjofur; Horton, Gregg; Hvidsten, Nils Arne; Jonsson, Ingi Runar; Jonsson, Nina; McKelvey, Simon; Næsje, Tor; Skaala, Øystein; Smith, Gordon W.; Sægrov, Harald; Stenseth, Nils Christian & Vøllestad, Leif Asbjørn (2014). Basin-scale phenology and effects of climate variability on global timing of initial seaward migration of Atlantic salmon (Salmo salar). Global Change Biology.  ISSN 1354-1013.  20(1), s 61- 75 . doi: 10.1111/gcb.12363 Vis sammendrag

  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. Atlantic salmon, smolt emigration, freshwater conditions, sea surface temperature, phenology, North Atlantic

• Marques, Reinaldo A. Gomes & Storvik, Geir Olve (2013). Particle move-reweighting strategies for online inference. Statistical research report (Universitetet i Oslo. Matematisk institut.  ISSN 0806-3842. Fulltekst i vitenarkiv.
• 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), s 168- 180 . doi: 10.1111/1365-2664.12004
• Aldrin, Magne; Mortensen, Bjørnar Tumanjan; Storvik, Geir Olve; Nedreaas, Kjell; Aglen, Asgeir & Aanes, Sondre (2012). Improving management decisions by predicting fish bycatch in the Barents Sea shrimp fishery. ICES Journal of Marine Science.  ISSN 1054-3139.  69(1), s 64- 74 . doi: 10.1093/icesjms/fsr172 Vis sammendrag

  When the bycatch of juvenile fish within the Barents Sea shrimp fishery is too large, the area is closed to fishing for a certain period. Bycatch is estimated from sampled trawl hauls, for which the shrimp yield is recorded, along with the total number of various bycatch fish species. At present, bycatch estimation is based on a simple estimator, the sum of the number of fish caught within the area of interest within a small time window, divided by the corresponding shrimp yield (in weight). No historical data are used. A model-based estimation is proposed in which spatio-temporal models are constructed for the variation in both the yield of shrimp and the amount of bycatch in space and time. The main effects are described through generalized additive models, and local dependence structures are specified through correlated random effects. Model estimation includes historical and recent data. Experiments with both simulated and real data show that the model-based estimator outperforms the present simple estimator when a low or moderate number of samples (e. g. <20) is available, whereas the two estimators are equally good when the number of samples is high.

• 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), s 2064- 2076 . doi: 10.1139/CJFAS-2012-0075
• Nielsen, Anders; Yoccoz, Nigel; Steinheim, Geir; Storvik, Geir Olve; Rekdal, Yngve; Angeloff, Michael; Pettorelli, Nathalie; Holand, Øystein & Mysterud, Atle (2012). Are responses of herbivores to environmental variability spatially consistent in alpine ecosystems?. Global Change Biology.  ISSN 1354-1013.  18(10), s 3050- 3062 . doi: 10.1111/j.1365-2486.2012.02733.x
• Storvik, Geir Olve (2012). Bayesian methods, In Marit Bragelien Veierød; Stian Lydersen & Petter Laake (ed.),  Medical statistics in clinical and epidemiological research.  Gyldendal Akademisk.  ISBN 9788205399594.  Kapittel 17.  s 559 - 584
• Storvik, Geir Olve (2012). Bootstrapping, In Marit Bragelien Veierød; Stian Lydersen & Petter Laake (ed.),  Medical statistics in clinical and epidemiological research.  Gyldendal Akademisk.  ISBN 9788205399594.  Kapittel 12.  s 402 - 428
• Villar, Jaime Otero; Jensen, Arne Johan; L'abee-Lund, Jan Henning; Stenseth, Nils Christian; Storvik, Geir Olve & Vøllestad, Leif Asbjørn (2012). Contemporary ocean warming and freshwater conditions are related to later sea age at maturity in Atlantic salmon spawning in Norwegian rivers. Ecology and Evolution.  ISSN 2045-7758.  2(9), s 2192- 2203 . doi: 10.1002/ece3.337 Fulltekst i vitenarkiv.
• Heier, Lise; Storvik, Geir Olve; Davis, Stephen; Viljugrein, Hildegunn; Ageyev, Vladimir; Klassovskaya, Evgeniya & Stenseth, Nils Christian (2011). Emergence, spread, persistence and fade-out of sylvatic plague in Kazakhstan. Proceedings of the Royal Society of London. Biological Sciences.  ISSN 0962-8452.  278(1720), s 2915- 2923 . doi: 10.1098/rspb.2010.2614
• Storvik, Geir Olve (2011). On the flexibility of Metropolis-Hastings acceptance probabilities in auxiliary variable proposal generation. Scandinavian Journal of Statistics.  ISSN 0303-6898.  38(2), s 342- 358 . doi: 10.1111/j.1467-9469.2010.00709.x
• Villar, Jaime Otero; Jensen, Arne Johan; L'abee-Lund, Jan Henning; Stenseth, Nils Christian; Storvik, Geir Olve & Vøllestad, Leif Asbjørn (2011). Quantifying the Ocean, Freshwater and Human Effects on Year-to-Year Variability of One-Sea-Winter Atlantic Salmon Angled in Multiple Norwegian Rivers. PLOS ONE.  ISSN 1932-6203.  6(8) . doi: 10.1371/journal.pone.0024005 Fulltekst i vitenarkiv.
• Steinbakk, Gunnhildur Högnadóttir & Storvik, Geir Olve (2009). Posterior Predictive p-values in Bayesian Hierarchical Models. Scandinavian Journal of Statistics.  ISSN 0303-6898.  36(2), s 320- 336 . doi: 10.1111/j.1467-9469.2008.00630.x
• Storvik, Bård; Storvik, Geir Olve & Fjørtoft, Roger (2009). On the Combination of Multisensor Data Using Meta-Gaussian Distributions. IEEE Transactions on Geoscience and Remote Sensing.  ISSN 0196-2892.  47(7), s 2372- 2379 . doi: 10.1109/TGRS.2009.2012699
• Storvik, Bård; Storvik, Geir & Fjørtoft, Roger Sverre (2008). On the combination of correlated images using meta-Gaussian distributions. Ukjent.
• Brekke, Camilla; Solberg, Anne H S & Storvik, Geir Olve (2007). Classifying Oil Spills and Look-Alikes in Envisat ASAR Images, In H Lacoste & L Ouwehand (ed.),  Proceedings of the Envisat Symposium 2007 - Montreux, Switzerland.  European Space Agency.  ISBN 0000000000.  kapittel.
• Hjermann, Dag Øystein; Melsom, Arne; Dingsør, Gjert Endre; Durant, Joël M.; Eikeset, Anne Maria; Røed, Lars Petter; Ottersen, Geir; Storvik, Geir & Stenseth, Nils Christian (2007). Fish and oil in the Lofoten-Barents Sea system: synoptic review of the effect of oil spills on fish populations. Marine Ecology Progress Series.  ISSN 0171-8630.  339, s 283- 299
• Hjermann, Dag Øystein; Melsom, Arne; Dingsør, Gjert Endre; Durant, Joël M.; Eikeset, Anne Maria; Røed, Lars Petter; Ottersen, Geir; Storvik, Geir & Stenseth, Nils Christian (2007). Fish and oil in the Lofoten-Barents Sea system: synoptic review of the effect of oil spills on fish populations. Marine Ecology Progress Series.  ISSN 0171-8630.  339, s 283- 299

Se alle arbeider i Cristin

• Mysterud, Atle; Bleka, Øyvind; Nielsen, Anders; Steinheim, Geir; Storvik, Geir Olve & Yoccoz, Nigel Gilles (2019). Climate and synchrony of lamb body mass, In Jon Olaf Olaussen (ed.),  Contributions in natural resource economics. Festschrift to Anders Skonhoft.  Fagbokforlaget.  ISBN 9788245024715.
• Storvik, Geir Olve (2019). Bayesian approaches to neural networks.
• Storvik, Geir Olve (2019). Bayesian approaches to neural networks.
• Storvik, Geir Olve (2019). Bayesian approaches to neural networks.
• Storvik, Geir Olve (2019). Education in Statistics and Data Science at the University of Oslo.
• Storvik, Geir Olve (2019). On the use of Bayesian methods in machine learning.
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models.
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models.
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models.
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models. Vis sammendrag

  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. Vis sammendrag

  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. Vis sammendrag

  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.

• Storvik, Geir Olve (2018). Preliminaries for Deep Neural Networks: Recapture of Linear Algebra, Gradient Descents and Generalized Linear Models.
• Storvik, Geir Olve (2018). Statistical analysis for Time series.
• Storvik, Geir Olve & Hubin, A. (2018). Bayesian model configuration, selection and averaging in complex regression contexts.
• Storvik, Geir Olve & Marques, Reinaldo (2018). On Monte Carlo Contributions for Real-time Probabilistic Inference.
• Storvik, Geir Olve; Vigeland, Magnus Dehli; Caliebe, Amke & Egeland, Thore (2018). Specification of mutation probabilities through Metropolis-Hastings steps.
• 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.  ISSN 0027-8424.  114(22), s E4322- E4323 . doi: 10.1073/pnas.1703865114
• Hubin, Aliaksandr & Storvik, Geir Olve (2017). Efficient mode jumping MCMC for Bayesian variable selection and model averaging in GLMM.
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2017). A novel GMJMCMC algorithm for Bayesian Logic Regression models.
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2017). A novel algorithmic approach to Bayesian Logic Regression.
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2017). A novel algorithmic approach to Bayesian Logic Regression.
• Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2017). Deep non-linear regression models in a Bayesian framework.
• Hubin, Aliaksandr; Storvik, Geir Olve & Grini, Paul Eivind (2017). Variable selection in binomial regression with latent Gaussian field models for analysis of epigenetic data.
• Hubin, Aliaksandr & Storvik, Geir Olve (2016). Efficient mode jumping MCMC for Bayesian variable selection in GLM with random effects models.
• Hubin, Aliaksandr & Storvik, Geir Olve (2016). On Mode Jumping in MCMC for Bayesian Variable Selection within GLMM.
• Hubin, Aliaksandr & Storvik, Geir Olve (2016). VARIABLE SELECTION IN BINOMIAL REGRESSION WITH LATENT GAUSSIAN FIELD MODELS FOR ANALYSIS OF EPIGENETIC DATA.
• Hubin, Aliaksandr & Storvik, Geir Olve (2016). Variable selection in logistic regression with a latent Gaussian field models with an application in epigenomics.
• Rognebakke, Hanne; Hirst, David; Aanes, Sondre & Storvik, Geir Olve (2016). Catch-at-age - Version 4.0: Technical Report. NR-notat. SAMBA/54/16.
• Storvik, Geir Olve; Løland, Anders; Lykkja, Ola Martin & Gjevestad, Jon Glenn Omholt (2016). SAVE – tracking vehicle movements for toll object detection using particle filter. NR-notat. SAMBA/18/16.
• Vanem, Erik; Glad, Ingrid Kristine & Storvik, Geir Olve (2016). Dynamical Linear Models for Condition Monitoring with Multivariate Sensor Data.
• Hubin, Aliaksandr & Storvik, Geir Olve (2015). On model selection in Hidden Markov Models with covariates.
• Hubin, Aliaksandr & Storvik, Geir Olve (2015). Variable selection in binomial regression with a latent Gaussian field models for analysis of epigenetic data.
• Hubin, Aliaksandr & Storvik, Geir Olve (2015). Variable selection in binomial regression with a latent Gaussian field models for analysis of epigenetic data.
• 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. Preprint Series in Statistics. 1. Fulltekst i vitenarkiv. Vis sammendrag

  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.

• Storvik, Geir Olve (2015). Estimation of fish abundance and demography in the Barents sea.
• Storvik, Geir Olve; Aanes, Sondre & Maisha, Peter Nyangweso (2015). Estimation of fish abundance and demography in the Barents sea.
• Storvik, Geir Olve & Marques, Reinaldo (2014). Estimation of static parameters using particle filters and a block independence approximation.
• Storvik, Geir Olve (2013). Computational tools for analysis of models with latent structures.
• Christensen, Arnfinn; Grunt, Hilde; Jødahl, Roar; Storvik, Geir Olve & Natvig, Bent (2011, 24. august). Tilfeldig! Neppe? En statistiker klarte å knekke vinnerkoden i et stort kanadisk skrapelotteri. Hvor tilfeldig er vinnertallene?. [Internett].  forskning.no.
• Rognebakke, Hanne Therese Wist; Hirst, David; Aldrin, Magne & Storvik, Geir (2011). Modelling catch at age for multiple stock.
• Rognebakke, Hanne Therese Wist; Hirst, David & Storvik, Geir Olve (2011). Catch-at-age - Version 2.0: Technical Report. NR-notat. SAMBA/44/11.
• Rognebakke, Hanne; Hirst, David; Storvik, Geir & Aldrin, Magne (2011). Catch-at-age for multiple stocks: Modelling Skrei and Coastal Cod simultaneously. NR-notat. SAMBA/46/11.
• Storvik, Geir Olve (2011, 24. august). Tilfeldig! Neppe?. [Internett].  Forskning.no.
• Villar, Jaime Otero; Jensen, Arne Johan; L'Abée-Lund, Jan Henning; Stenseth, Nils Christian; Storvik, Geir Olve & Vøllestad, Leif Asbjørn (2010). Contemporary ocean warming and freshwater conditions contribute to delay the completion of maturation in Atlantic salmon throughout the Norwegian range of distribution.
• Heier, Lise; Storvik, Geir Olve; Davis, Stephen; Viljugrein, Hildegunn; Ageyev, Vladimir; Klassovskaya, Evgeniya & Stenseth, Nils Christian (2010). Emergence, Spread, Persistence and Fade-out of Sylvatic Plague in Kazakhstan.
• Storvik, Geir Olve (2010). DNA som bevismateriale - Sannsynlighetsregning i rettsalen.
• Storvik, Geir Olve (2010). On the flexibility of acceptance probabilities in auxiliary variable Metropolis-Hastings algorithms.
• Storvik, Geir Olve; Heier, Lise; Stenseth, Nils Christian & Kvaal, Bård Øyvind (2010). Bayesian analysis of plague in Kazakhstan.
• Storvik, Geir Olve; Heier, Lise; Viljugrein, Hildegunn & Stenseth, Nils Christian (2010). Bayesian analysis of plague in Kazakhstan.
• Villar, Jaime Otero; Antonsson, Thorulfur; Armstrong, John; Arnason, Fridthjofur; Arnekleiv, Jo Vegar; Baglinière, Jean-Luc; Caballero, Pablo; Castro-Santos, Ted; Dempson, Brian; Erkinaro, Jaakko; Gudjonsson, Sigurdur; Hvidsten, Nils Arne; Jensen, Arne Johan; Jokikokko, Erkki; Jonsson, Ingi Runar; Kocik, John; L'Abée-Lund, Jan Henning; Lamberg, Anders; Letcher, Benjamin; Niemela, Eero; Romakkaniemi, Atso; Russell, Ian; Stenseth, Nils Christian; Storvik, Geir Olve; Veselov, Alexey & Vøllestad, Leif Asbjørn (2010). Environmental effects on ocean entry of Atlantic salmon (Salmo salar) smolt across its range of distribution.
• Villar, Jaime Otero; Jensen, Arne J.; L'Abée-Lund, Jan Henning; Stenseth, Nils Christian; Storvik, Geir Olve & Vøllestad, Leif Asbjørn (2009). Factors affecting year-to-year and within river variability of one-sea-winter Atlantic salmon in Norwegian rivers.
• Hirst, David; Storvik, Geir Olve; Rognebakke, Hanne & Aldrin, Magne (2009). A Bayesian hierarchical modelling approach to estimating landings- and discards-at-age.
• Storvik, Geir Olve (2009). DNA som bevismateriale - Sannsynlighetsregning i praksis.
• Storvik, Geir Olve (2009). On the flexibility of acceptance probabilities in auxiliary variable Metropolis-Hastings algorithms.
• Storvik, Geir Olve (2009). Plague in Kazakhstan - A case study.
• Storvik, Geir Olve (2009). Quantification of evidence when suspects are found by searches through DNA databases.
• Storvik, Geir Olve (2009). Sekvensiell Monte Carlo (eller Partikkelfiltre).
• Storvik, Geir Olve (2008). Modelling Pollock egg counts from the western Gulf of Alaska by a zero-inflated Bayesian hierarchical space-time model.
• Storvik, Geir Olve (2008). On the flexibility of importance weights in importance sampling and Markov chain Monte Carlo.
• Hirst, David; Rognebakke, Hanne Therese Wist; Storvik, Geir Olve & Aldrin, Magne (2007). Implementing the catch-at-age program for FRS Aberdeen. NR-notat. SAMBA/09/2007.
• Storvik, Geir Olve (2007). DNA som bevismateriale - Sannsynlighetsregning i rettssalen.
• Storvik, Geir Olve (2007). DNA som bevismateriale Sannsynlighetsregning i rettssalen.
• Storvik, Geir Olve (2007). Hvorfor data + data = sant: Fra telling av fisk til DNA-profiler av voldsmenn.
• Storvik, Geir Olve (2007). Intro til Bayesiansk Statistikk.
• Storvik, Geir Olve (2007). Modelling Pollock egg counts from the western Gulf of Alaska by a zero-inflated Bayesian hierarchical space-time model.
• Storvik, Geir Olve (2007). Posterior and Prior Predictive p-values in Bayesian Hierarchical models.

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