74 citations · 197 across the 13 of their papers we have counts for
13 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…
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…
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…
Identifying important predictors in large data bases -- multiple testing and model selection
Malgorzata Bogdan, Florian Frommlet
This is a chapter of the forthcoming Handbook of Multiple Testing. We consider a variety of model selection strategies in a high-dimensional setting, where the number of potential…
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…
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…