25 citations · 94 across the 21 of their papers we have counts for
6 papers · 1 filter
Bayesian Fractional Polynomials for Optimal Dosage Estimation with Fish Nutrition Applications
Aliaksandr Hubin, Åshild Krogdahl, Guro Løkka +1
The problem of optimal dosage estimation arises in diverse scientific domains, from pharmacology and toxicology to aquaculture and environmental studies. Statistical modeling of no…
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…
Bayesian Generalized Nonlinear Models Offer Basis Free SINDy With Model Uncertainty
Aliaksandr Hubin
Sparse Identification of Nonlinear Dynamics (SINDy) has become a standard methodology for inferring governing equations of dynamical systems from observed data using statistical mo…
Fractional Polynomials Models as Special Cases of Bayesian Generalized Nonlinear Models
Aliaksandr Hubin, Georg Heinze, Riccardo De Bin
We propose a framework for fitting fractional polynomials models as special cases of Bayesian Generalized Nonlinear Models, applying an adapted version of the Genetically Modified…
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…
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…