21 citations · 66 across the 14 of their papers we have counts for
14 papers
FBMS: An R Package for Flexible Bayesian Model Selection and Model Averaging
Florian Frommlet, Jon Lachmann, Geir Storvik +1
The FBMS R package facilitates Bayesian model selection and model averaging in complex regression settings by employing a variety of Monte Carlo model exploration methods. At its c…
Sparsifying Bayesian neural networks with latent binary variables and normalizing flows
Lars Skaaret-Lund, Geir Storvik, Aliaksandr Hubin
Artificial neural networks (ANNs) are powerful machine learning methods used in many modern applications such as facial recognition, machine translation, and cancer diagnostics. A…
Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty
Aliaksandr Hubin, Geir Storvik
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 infe…
Faster estimation of the Knorr-Held Type IV space-time model
Fredrik Lohne Aanes, Geir Storvik
In this paper we study the type IV Knorr Held space time models. Such models typically apply intrinsic Markov random fields and constraints are imposed for identifiability. INLA is…
A subsampling approach for Bayesian model selection
Jon Lachmann, Geir Storvik, Florian Frommlet +1
It is common practice to use Laplace approximations to compute marginal likelihoods in Bayesian versions of generalised linear models (GLM). Marginal likelihoods combined with mode…
A sequential Monte Carlo approach to estimate a time varying reproduction number in infectious disease models: the Covid-19 case
Geir Storvik, Alfonso Diz-Lois Palomares, Solveig Engebretsen +5
During the first months, the Covid-19 pandemic has required most countries to implement complex sequences of non-pharmaceutical interventions, with the aim of controlling the trans…
Reversible Genetically Modified Mode Jumping MCMC
Aliaksandr Hubin, Florian Frommlet, Geir Storvik
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…
A Bayesian binomial regression model with latent Gaussian processes for modelling DNA methylation
Aliaksandr Hubin, Geir O Storvik, Paul E Grini +1
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 bin…
Flexible Bayesian Nonlinear Model Configuration
Aliaksandr Hubin, Geir Storvik, Florian Frommlet
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 ofte…
Combining Model and Parameter Uncertainty in Bayesian Neural Networks
Aliaksandr Hubin, Geir Storvik
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 infe…
Deep Bayesian regression models
Aliaksandr Hubin, Geir Storvik, Florian Frommlet
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…
A novel algorithmic approach to Bayesian Logic Regression
Aliaksandr Hubin, Geir Storvik, Florian Frommlet
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 e…
Estimating the marginal likelihood with Integrated nested Laplace approximation (INLA)
Aliaksandr Hubin, Geir Storvik
The marginal likelihood is a well established model selection criterion in Bayesian statistics. It also allows to efficiently calculate the marginal posterior model probabilities t…
Mode jumping MCMC for Bayesian variable selection in GLMM
Aliaksandr Hubin, Geir Storvik
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…