Gudmund Horn Hermansen

• Dahl, Marianne; Gleditsch, Kristian Skrede; Hermansen, Gudmund Horn & Rudolfsen, Ida (2026). Only a Moment in Time? The Changing Effectiveness of Mass Mobilization on Transitions to Democracy. Comparative Political Studies. ISSN 0010-4140. doi: 10.1177/00104140261455877. Full text in Research Archive
• Hjort, Anders Dahl; Hermansen, Gudmund Horn; Pensar, Johan & Williams, Jonathan P. (2025). Uncertainty quantification in automated valuation models with spatially weighted conformal prediction. International Journal of Data Science and Analytics (JDSA). ISSN 2364-415X. 20, p. 7089–7106. doi: 10.1007/s41060-025-00862-4. Full text in Research Archive Show summary

  Nonparametric machine learning models, such as random forests and gradient boosted trees, are frequently used to estimate house prices due to their predictive accuracy, but a main drawback of such methods is their limited ability to quantify prediction uncertainty. Conformal prediction (CP) is a model-agnostic framework for constructing confidence sets around predictions of machine learning models with minimal assumptions. However, due to the spatial dependencies observed in house prices, direct application of CP leads to confidence sets that are not calibrated everywhere, i.e., the confidence sets will be too large in certain geographical regions and too small in others. We survey various approaches to adjust the CP confidence set to account for this and demonstrate their performance on a data set from the housing market in Oslo, Norway. Our findings indicate that calibrating the confidence sets on a spatially weighted version of the non-conformity scores makes the coverage more consistently calibrated across geographical regions. We also perform a simulation study on synthetically generated sale prices to empirically explore the performance of CP on housing market data under idealized conditions with known data-generating mechanisms.

• Kendall, Emmett B.; Williams, Jonathan P.; Hermansen, Gudmund Horn; Bois, Frederic & Thanh, Vo Hong (2024). Beyond Time-Homogeneity for Continuous-Time Multistate Markov Models. Journal of Computational And Graphical Statistics (JCGS). ISSN 1061-8600. doi: 10.1080/10618600.2024.2388609. Full text in Research Archive Show summary

  Abstract–Multistate Markov models are a canonical parametric approach for data modeling of observed or latent stochastic processes supported on a finite state space. Continuous-time Markov processes describe data that are observed irregularly over time, as is often the case in longitudinal medical data, for example. Assuming that a continuous-time Markov process is time-homogeneous, a closed-form likelihood function can be derived from the Kolmogorov forward equations—a system of differential equations with a well-known matrix-exponential solution. Unfortunately, however, the forward equations do not admit an analytical solution for continuous-time, time- inhomogeneous Markov processes, and so researchers and practitioners often make the simplifying assumption that the process is piecewise time-homogeneous. In this article, we provide intuitions and illustrations of the potential biases for parameter estimation that may ensue in the more realistic scenario that the piecewise-homogeneous assumption is violated, and we advocate for a solution for likelihood computation in a truly time-inhomogeneous fashion. Particular focus is afforded to the context of multistate Markov models that allow for state label misclassifications, which applies more broadly to hidden Markov models (HMMs), and Bayesian computations bypass the necessity for computationally demanding numerical gradient approximations for obtaining maximum likelihood estimates (MLEs). Supplemental materials are available online.

• Williams, Jonathan P.; Hermansen, Gudmund Horn; Strand, Håvard; Clayton, Govinda & Nygård, Håvard Mokleiv (2024). BAYESIAN HIDDEN MARKOV MODELS FOR LATENT VARIABLE LABELING ASSIGNMENTS IN CONFLICT RESEARCH: APPLICATION TO THE ROLE CEASEFIRES PLAY IN CONFLICT DYNAMICS. Annals of Applied Statistics. ISSN 1932-6157. 18(3), p. 2034–2061. doi: 10.1214/23-AOAS1869. Full text in Research Archive Show summary

  A crucial challenge for solving problems in conflict research is in leveraging the semisupervised nature of the data that arise. Observed response data, such as counts of battle deaths over time, indicate latent processes of interest, such as intensity and duration of conflicts, but defining and labeling instances of these unobserved processes requires nuance and imprecision. The availability of such labels, however, would make it possible to study the effect of intervention-related predictors—such as ceasefires—directly on conflict dynamics (e.g., latent intensity) rather than through an intermediate proxy, like observed counts of battle deaths. Motivated by this problem and the new availability of the ETH-PRIO Civil Conflict Ceasefires data set, we propose a Bayesian autoregressive (AR) hidden Markov model (HMM) framework as a sufficiently flexible machine learning approach for semisupervised regime labeling with uncertainty quantification. We motivate our approach by illustrating the way it can be used to study the role that ceasefires play in shaping conflict dynamics. This ceasefires data set is the first systematic and globally comprehensive data on ceasefires, and our work is the first to analyze this new data and to explore the effect of ceasefires on conflict dynamics in a comprehensive and cross-country manner.

• Åbjørsbråten, Knut Sindre; Skaaraas, Gry Helen Enger Syverstad; Cunen, Celine Marie Løken; Bjørnstad, Daniel Marelius; Binder, Kristin Maria Gullestad & Bojarskaite, Laura [Show all 15 contributors for this article] (2022). Impaired astrocytic Ca2+ signaling in awake-behaving Alzheimer’s disease transgenic mice. eLIFE. doi: 10.7554/eLife.75055. Full text in Research Archive Show summary

  Increased astrocytic Ca2+ signaling has been shown in Alzheimer’s disease mouse models, but to date no reports have characterized behaviorally induced astrocytic Ca2+ signaling in such mice. Here, we employ an event-based algorithm to assess astrocytic Ca2+ signals in the neocortex of awake-behaving tg-ArcSwe mice and non-transgenic wildtype littermates while monitoring pupil responses and behavior. We demonstrate an attenuated astrocytic Ca2+ response to locomotion and an uncoupling of pupil responses and astrocytic Ca2+ signaling in 15-month-old plaque-bearing mice. Using the genetically encoded fluorescent norepinephrine sensor GRABNE, we demonstrate a reduced norepinephrine signaling during spontaneous running and startle responses in the transgenic mice, providing a possible mechanistic underpinning of the observed reduced astrocytic Ca2+ responses. Our data points to a dysfunction in the norepinephrine–astrocyte Ca2+ activity axis, which may account for some of the cognitive deficits observed in Alzheimer’s disease.

• Bjørnstad, Daniel Marelius; Åbjørsbråten, Knut Sindre; Hennestad, Eivind; Cunen, Celine; Hermansen, Gudmund Horn & Bojarskaite, Laura [Show all 9 contributors for this article] (2021). Begonia - a two-photon imaging analysis pipeline for astrocytic Ca2+ signals. Frontiers in Cellular Neuroscience. 15. doi: 10.3389/fncel.2021.681066. Full text in Research Archive Show summary

  Imaging the intact brain of awake behaving mice without the dampening effects of anesthesia, has revealed an exceedingly rich repertoire of astrocytic Ca 2+ signals. Analyzing and interpreting such complex signals pose many challenges. Traditional analyses of fluorescent changes typically rely on manually outlined static region-of-interests, but such analyses fail to capture the intricate spatiotemporal patterns of astrocytic Ca 2+ dynamics. Moreover, all astrocytic Ca 2+ imaging data obtained from awake behaving mice need to be interpreted in light of the complex behavioral patterns of the animal. Hence processing multimodal data, including animal behavior metrics, stimulation timings, and electrophysiological signals is needed to interpret astrocytic Ca 2+ signals. Managing and incorporating these data types into a coherent analysis pipeline is challenging and time-consuming, especially if research protocols change or new data types are added. Here, we introduce Begonia, a MATLAB-based data management and analysis toolbox tailored for the analyses of astrocytic Ca 2+ signals in conjunction with behavioral data. The analysis suite includes an automatic, event-based algorithm with few input parameters that can capture a high level of spatiotemporal complexity of astrocytic Ca 2+ signals. The toolbox enables the experimentalist to quantify astrocytic Ca 2+ signals in a precise and unbiased way and combine them with other types of time series data.

• Hermansen, Gudmund Horn; Knutsen, Carl Henrik & Nygård, Håvard Mokleiv (2021). Characterizing and Assessing Temporal Heterogeneity: Introducing a Change Point Framework, with Applications on the Study of Democratization. Political Analysis. ISSN 1047-1987. 29(4), p. 485–504. doi: 10.1017/pan.2020.39. Full text in Research Archive Show summary

  Various theories in political science point to temporal heterogeneity in relationships of interest. Yet, empirical research typically ignores such heterogeneity or employs fairly crude measures to evaluate it. Advances in models for change point detection offer opportunities to study temporal heterogeneity more carefully. We customize a recent such method for political science purposes, for instance so that it accommodates panel data, and provide an accompanying R-package. We evaluate the methodology, and how it behaves when different assumptions about the number and abrupt nature of change points are violated, by using simulated data. Importantly, the methodology allows us to evaluate changes to different quantities of interest (for various estimators). It also allows us to provide comprehensive estimates concerning uncertainty in the timing and size of changes. We illustrate the utility of this flexible change point methodology on two types of regression models (Probit and OLS) in two empirical applications. We first re-investigate the proposition by Albertus (2017) that labor-dependent agriculture had a more pronounced negative effect on democratic survival before the “third wave of democratization.” Next, we utilize data extending from the French revolution to the present, from V-Dem, to examine the time-variant nature of the income–democracy relationship.

• Bojarskaite, Laura; Bjørnstad, Daniel Marelius; Pettersen, Klas Henning; Cunen, Celine; Hermansen, Gudmund Horn & Åbjørsbråten, Knut Sindre [Show all 12 contributors for this article] (2020). Astrocytic Ca2+ signaling is reduced during sleep and is involved in the regulation of slow wave sleep. Nature Communications. 11. doi: 10.1038/s41467-020-17062-2. Full text in Research Archive Show summary

  Astrocytic Ca2+ signaling has been intensively studied in health and disease but has not been quantified during natural sleep. Here, we employ an activity-based algorithm to assess astrocytic Ca2+ signals in the neocortex of awake and naturally sleeping mice while monitoring neuronal Ca2+ activity, brain rhythms and behavior. We show that astrocytic Ca2+ signals exhibit distinct features across the sleep-wake cycle and are reduced during sleep compared to wakefulness. Moreover, an increase in astrocytic Ca2+ signaling precedes transitions from slow wave sleep to wakefulness, with a peak upon awakening exceeding the levels during whisking and locomotion. Finally, genetic ablation of an important astrocytic Ca2+ signaling pathway impairs slow wave sleep and results in an increased number of microarousals, abnormal brain rhythms, and an increased frequency of slow wave sleep state transitions and sleep spindles. Our findings demonstrate an essential role for astrocytic Ca2+ signaling in regulating slow wave sleep.

• Cunen, Celine Marie Løken; Hermansen, Gudmund Horn & Hjort, Nils Lid (2018). Confidence distributions for change-points and regime shifts. Journal of Statistical Planning and Inference. ISSN 0378-3758. 195, p. 14–34. doi: 10.1016/j.jspi.2017.09.009. Full text in Research Archive Show summary

  Suppose observations y 1 ,...,y n stem from a parametric model f ( y,θ ), with the parameter taking one value θ L for y 1 ,...,y τ and another value θ R for y τ +1 ,...,y n . This article provides and examines two different general strategies for not merely estimating the break point τ but also to complement such an estimate with full confidence distributions, both for the change-point τ and for associated measures of differences between the two levels of θ . The first idea worked with involves testing homogeneity for the two segments to the left and the right of a candidate change-point value at a fine-tuned level of significance. Carrying out such a scheme requires having a goodness-of-fit test for constancy of the θ parameter over a segment of indices, and we also develop classes of such tests. These also have some independent interest. The second general method uses the log-likelihood function, profiled over the other parameters, and we show how this may lead to confidence inference for τ . Our methods are illustrated for four real data stories, with these meeting different types of challenges.

• Olsen, Håvard Goodwin & Hermansen, Gudmund Horn (2017). Recent Advancements to Nonparametric Modeling of Interactions Between Reservoir Parameters. Quantitative Geology and Geostatistics. ISSN 0924-1973. 19, p. 653–669. doi: 10.1007/978-3-319-46819-8_44. Full text in Research Archive Show summary

  We demonstrate recent advances in nonparametric density estimation and illustrate their potential in the petroleum industry. Here, traditional parametric models and standard kernel methodology may often prove too limited. This is especially the case for data possessing certain complex structures, such as pinch-outs, nonlinearity, and heteroscedasticity. In this paper, we will focus on the Cloud Transform (CT) with directional smoothing and Local Gaussian Density Estimator (LGDE). These are flexible nonparametric methods for density (and conditional distribution) estimation that are well suited for data types commonly encountered in reservoir modeling. Both methods are illustrated with real and synthetic data sets.

• Hauge, Vera Louise & Hermansen, Gudmund Horn (2017). Machine Learning Methods for Sweet Spot Detection: A Case Study. Quantitative Geology and Geostatistics. ISSN 0924-1973. 19, p. 573–588. doi: 10.1007/978-3-319-46819-8_38. Full text in Research Archive Show summary

  In the geosciences, sweet spots are defined as areas of a reservoir that represent best production potential. From the outset, it is not always obvious which reservoir characteristics that best determine the location, and influence the likelihood, of a sweet spot. Here, we will view detection of sweet spots as a supervised learning problem and use tools and methodology from machine learning to build data-driven sweet spot classifiers. We will discuss some popular machine learning methods for classification including logistic regression, k-nearest neighbors, support vector machine, and random forest. We will highlight strengths and shortcomings of each method. In particular, we will draw attention to a complex setting and focus on a smaller real data study with limited evidence for sweet spots, where most of these methods struggle. We will illustrate a simple solution where we aim at increasing the performance of these by optimizing for precision. In conclusion, we observe that all methods considered need some sort of preprocessing or additional tuning to attain practical utility. While the application of support vector machine and random forest shows a fair degree of promise, we still stress the need for caution in naive use of machine learning methodology in the geosciences.

• Vonnet, Julie & Hermansen, Gudmund Horn (2015). Using predictive analytics to unlock unconventional plays. First Break. ISSN 0263-5046. 33(2), p. 87–92. Full text in Research Archive Show summary

  Julie Vonnet and Gudmund Hermansen introduce a multi-disciplinary, integrated reservoir modelling workflow that gathers data of different natures, sources and scale in order to predict sweet spots locations. The dramatic expansion in computing power over the past two decades and the huge amount of data generated within organisations have led to a proliferation of new methods to identify patterns and trends among these large datasets. Data mining and predictive analytics are cross-disciplinary approaches that consist of advanced mathematical and statistical methods that retrace patterns from petabytes of data. Such methods and algorithms are able to extract information, correlations and interplays and turn them into structured sets of interactions for predicting the behaviour of a system – even under unknown conditions.

• Hermansen, Gudmund Horn; Hjort, Nils Lid & Kjesbu, Olav Sigurd (2015). Recent advances in statistical methodology applied to the Hjort liver index time series (1859-2012) and associated influential factors. Canadian Journal of Fisheries and Aquatic Sciences. ISSN 0706-652X. 73(2), p. 279–295. doi: 10.1139/cjfas-2015-0086. Full text in Research Archive
• Hermansen, Gudmund Horn & Hjort, Nils Lid (2015). Bernshteĭn-von Mises theorems for nonparametric function analysis via locally constant modelling: A unified approach. Journal of Statistical Planning and Inference. ISSN 0378-3758. 166, p. 138–157. doi: 10.1016/j.jspi.2015.02.007. Full text in Research Archive Show summary

  Various statistical models involve a certain function, say f, like the mean regression as a function of a covariate, the hazard rate as a function of time, the spectral density of a time series as a function of frequency, or an intensity as a function of geographical position, etc. Such functions are often modelled parametrically, whether for frequentist or Bayesian uses, and under weak conditions there are so-called Bernshteĭn–von Mises theorems implying that these two approaches are large-sample equivalent. Results of this nature do not necessarily hold up in nonparametric and high-dimensional setups, however. The aim of the present paper is to exhibit a unified framework and methodology for both frequentist and Bayesian nonparametric analysis, involving priors that set f constant over windows, and where the number m of such windows grows with sample size n. Applications include nonparametric regression, maximum likelihood with nonparametrically varying parameter functions, hazard rates being functions of covariates, and nonparametric analysis of stationary time series. We work out conditions on the number and sizes of the windows under which Bernshteĭn–von Mises type theorems can be established, with the prior changing with sample size via the growing number of windows. These conditions entail e.g. that if m∝nα, then View the MathML source is required. Illustrations of the general methodology are given, including setups with nonparametric regression, hazard rate estimation, and inference about frequency spectra for stationary time series.

• Walker, Sam-Erik; Hermansen, Gudmund Horn & Hjort, Nils Lid (2015). Model selection and verification for ensemble based probabilistic forecasting of air pollution in Oslo, Norway. In Samaniego, Francisco J. (Eds.), Proceedings of the 60th World Statistics Congress of the International Statistical Institute, ISI2015. The International Statistical Institute. ISSN 9789073592353. p. 3131–3136. Full text in Research Archive Show summary

  In this paper, we discuss building time series models for forecasting of air pollution during wintertime conditions in Oslo, Norway, using ensembles of air pollution model data. Since such ensembles becomes increasingly available as part of regular air quality forecast modelling, it is important to build properly calibrated statistical models utilising such data. In particular, we focus on model selection using the Akaike and Bayesian information criteria, and verification of the forecasts using Probability Integral Transform (PIT) histograms and Brier scores. Three time series models are considered, using ensemble mean values as a primary covariate in a linear regression setting explaining observations, and modelling the residual errors as an autoregressive process, using either a constant variance; a timevarying (heteroscedastic) variance only depending on the ensemble variances; or as a combination of both. We show that for the limited, although representative, data analysed, the model incorporating both terms, seems to have an edge according to the model selection criteria and forecast verification tools used. Finally, we briefly discuss the possibility of introducing more focused model selection criteria for these types of models and data.

• Hermansen, Gudmund Horn; Hjort, Nils Lid & Jullum, Martin (2015). Parametric or nonparametric: the FIC approach for stationary time series. In Samaniego, Francisco J. (Eds.), Proceedings of the 60th World Statistics Congress of the International Statistical Institute, ISI2015. The International Statistical Institute. ISSN 9789073592353. p. 4827–4832. Full text in Research Archive Show summary

  We seek to narrow the gap between parametric and nonparametric modelling of stationary time series processes. The approach is inspired by recent advances in focused inference and model selection techniques. Our paper generalises and extends current work by developing a new version of the focused information criterion (FIC), directly comparing the performance of both parametric and nonparametric time series models. This is achieved by comparing the mean squared error for estimating a focus parameter under consideration, for each candidate model. In particular this yields FIC formulae for covariances or correlations at specified lags, for the probability of reaching a threshold, etc. Suitable weighted average versions, the AFIC, also lead to model selection strategies for finding the best model for the purpose of estimating e.g. a sequence of correlations.

• Bjørvik, Linn Marie; Dale, Svein; Hermansen, Gudmund Horn; Munishi, Pantaleo K. T. & Moe, Stein Ragnar (2014). Bird flight initiation distances in relation to distance from human settlements in a Tanzanian floodplain habitat. Journal of Ornithology. ISSN 2193-7192. 156, p. 239–246. doi: 10.1007/s10336-014-1121-1. Full text in Research Archive

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• Foldnes, Njål; Grønneberg, Steffen; Hermansen, Gudmund Horn & Wellen, Einar Christopher (2024). Statistikk og dataanalyse. Cappelen Damm Akademisk. ISBN 9788202780043. Full text in Research Archive
• Foldnes, Njål; Grønneberg, Steffen & Hermansen, Gudmund Horn (2018). Statistikk og Dataanalyse. Cappelen Damm Akademisk. ISBN 9788202512682. 541 p. Full text in Research Archive

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• Dahl, Marianne; Gleditsch, Kristian Skrede; Hermansen, Gudmund Horn & Rudolfsen, Ida (2023). Ebb and flow? The effectiveness of MASS mobilization on transitions to democracy. Full text in Research Archive
• Hermansen, Gudmund Horn; Nygård, Håvard Mokleiv; Williams, Jonathan; Rustad, Siri Camilla Aas & Clayton, Govinda (2020). The Dynamics of Civil Wars: A Bayesian hidden Markov model applied to the pattern of conflict and the role of ceasefires. Full text in Research Archive
• Hermansen, Gudmund Horn (2016). Focused Information Criteria for Time Series. Full text in Research Archive
• Hermansen, Gudmund Horn (2016). Focused model selection strategies for time series processes. Full text in Research Archive
• Olsen, Håvard Goodwin & Hermansen, Gudmund Horn (2016). Recent advancements to non-parametric modelling of interactions between reservoir parameters. Full text in Research Archive
• Hauge, Vera Louise & Hermansen, Gudmund Horn (2016). Machine learning methods for sweet spot detection: a case study. Full text in Research Archive
• Almendral-Vazquez, Ariel; Abrahamsen, Petter; Dahle, Pål & Hermansen, Gudmund Horn (2015). A continuous model for well depths: theory and application to well repositioning. Full text in Research Archive
• Walker, Sam-Erik; Hjort, Nils Lid & Hermansen, Gudmund Horn (2015). Model selection and verification for ensemble based probabilistic forecasting of air pollution in Oslo, Norway. Full text in Research Archive
• Hermansen, Gudmund Horn; Nygård, Håvard Mokleiv & Ghai, Chris (2020). Finding the needle in the haystack: Fine-tuning transformers to classify protest events in a sea ofnews articles, with Bayesian uncertainty measures. Universitetet i Oslo. Full text in Research Archive
• Nygård, Håvard Mokleiv; Knutsen, Carl Henrik & Hermansen, Gudmund Horn (2019). Characterizing and Assessing Temporal Heterogeneity: Introducing a Change Point Framework, with Applications on the Study of Democratization. Varieties of Democracy (V-Dem) Institute. Full text in Research Archive
• Olsen, Håvard Goodwin & Hermansen, Gudmund Horn (2017). Updates to density estimations in RMS. Norsk Regnesentral. Full text in Research Archive
• Hauge, Vera Louise & Hermansen, Gudmund Horn (2016). Sweet spot detection by support vector machine. Norsk Regnesentral. Full text in Research Archive
• Abrahamsen, Petter; Dahle, Pål; Hauge, Vera Louise; Hermansen, Gudmund Horn; Kvernelv, Vegard Berg & Vazquez, Ariel Almendral (2016). Cohiba User Manual Version 5.4. Norsk Regnesentral. Full text in Research Archive
• Abrahamsen, Petter; Dahle, Pål; Hauge, Vera Louise; Hermansen, Gudmund Horn; Vigsnes, Maria & Almendral-Vazquez, Ariel (2015). Cohiba User Manual Version 5.3. Norsk Regnesentral. Full text in Research Archive
• Abrahamsen, Petter; Dahle, Pål; Hauge, Vera Louise; Hermansen, Gudmund Horn; Vigsnes, Maria & Vazquez, Ariel Almendral (2015). Cohiba User Manual, Version 5.1. Norsk Regnesentral. Full text in Research Archive
• Abrahamsen, Petter; Dahle, Pål; Hauge, Vera Louise; Hermansen, Gudmund Horn; Vigsnes, Maria & Almendral-Vazquez, Ariel (2015). Cohiba User Manual Version 5.2. Norsk Regnesentral. Full text in Research Archive
• Hjort, Nils Lid & Hermansen, Gudmund Horn (2014). Model Selection and Bayesian Nonparametrics for Time Series and Nonstandard Regression Models. Universitetet i Oslo. Full text in Research Archive
• Hermansen, Gudmund Horn & Østvold, Bjarte M. (2014). INPRED: Intelligent prediction for package transport. Norsk Regnesentral. Full text in Research Archive
• Abrahamsen, Petter; Dahle, Pål; Georgsen, Frode; Hermansen, Gudmund Horn & Myrseth, Inge (2013). Cohiba User Manual Version 4.1. Norsk Regnesentral. Full text in Research Archive
• Abrahamsen, Petter; Dahle, Pål; Hauge, Vera Louise; Hermansen, Gudmund Horn & Vigsnes, Maria (2013). COHIBA technical manual (SAND/12/13). Norsk Regnesentral. Full text in Research Archive
• Abrahamsen, Petter; Dahle, Pål; Hermansen, Gudmund Horn; Hauge, Vera Louise & Vigsnes, Maria (2013). COHIBA user manual version 4.2 (SAND/10/13). Norsk Regnesentral. Full text in Research Archive

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