Gudmund Horn Hermansen

• 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. ISSN 2050-084X. 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. ISSN 1662-5102. 15. doi: 10.3389/fncel.2021.681066. Full text in Research Archive
• 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
• 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. ISSN 2041-1723. 11. doi: 10.1038/s41467-020-17062-2. Full text in Research Archive
• 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
• 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. 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. 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.

• 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 978-90-73592-35-3. p. 3131–3136. 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 978-90-73592-35-3. p. 4827–4832. 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.

• 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.
• 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. 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.

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

• 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.

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

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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.
• 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.
• Olsen, Håvard Goodwin & Hermansen, Gudmund Horn (2016). Recent advancements to non-parametric modelling of interactions between reservoir parameters.
• Hauge, Vera Louise & Hermansen, Gudmund Horn (2016). Machine learning methods for sweet spot detection: a case study.
• Hermansen, Gudmund Horn (2016). Focused Information Criteria for Time Series.
• Hermansen, Gudmund Horn (2016). Focused model selection strategies for time series processes.
• Almendral-Vazquez, Ariel; Abrahamsen, Petter; Dahle, Pål & Hermansen, Gudmund Horn (2015). A continuous model for well depths: theory and application to well repositioning.
• 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 .
• 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 of news articles, with Bayesian uncertainty measures. Universitetet i Oslo.
• 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.
• Olsen, Håvard Goodwin & Hermansen, Gudmund Horn (2017). Updates to density estimations in RMS. Norsk Regnesentral.
• Abrahamsen, Petter; Dahle, Pål; Hauge, Vera Louise; Hermansen, Gudmund Horn; Kvernelv, Vegard Berg & Almendral Vazquez, Ariel (2016). Cohiba User Manual Version 5.4. Norsk Regnesentral.
• Hauge, Vera Louise & Hermansen, Gudmund Horn (2016). Sweet spot detection by support vector machine. Norsk Regnesentral.
• Abrahamsen, Petter; Dahle, Pål; Hauge, Vera Louise; Hermansen, Gudmund Horn; Vigsnes, Maria & Almendral-Vazquez, Ariel (2015). Cohiba User Manual Version 5.3. Norsk Regnesentral.
• Abrahamsen, Petter; Dahle, Pål; Hauge, Vera Louise; Hermansen, Gudmund Horn; Vigsnes, Maria & Almendral-Vazquez, Ariel (2015). Cohiba User Manual Version 5.2. Norsk Regnesentral.
• Abrahamsen, Petter; Dahle, Pål; Hauge, Vera Louise; Hermansen, Gudmund Horn; Vigsnes, Maria & Almendral Vazquez, Ariel (2015). Cohiba User Manual, Version 5.1. Norsk Regnesentral.
• Hermansen, Gudmund Horn & Østvold, Bjarte M. (2014). INPRED: Intelligent prediction for package transport. Norsk Regnesentral.
• Hjort, Nils Lid & Hermansen, Gudmund Horn (2014). Model Selection and Bayesian Nonparametrics for Time Series and Nonstandard Regression Models. Universitetet i Oslo.
• Abrahamsen, Petter; Dahle, Pål; Hauge, Vera Louise; Hermansen, Gudmund Horn & Vigsnes, Maria (2013). COHIBA technical manual (SAND/12/13). Norsk Regnesentral.
• Abrahamsen, Petter; Dahle, Pål; Hermansen, Gudmund Horn; Hauge, Vera Louise & Vigsnes, Maria (2013). COHIBA user manual version 4.2 (SAND/10/13). Norsk Regnesentral.
• Abrahamsen, Petter; Dahle, Pål; Georgsen, Frode; Hermansen, Gudmund Horn & Myrseth, Inge (2013). Cohiba User Manual Version 4.1. Norsk Regnesentral.

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