17 citations · 19 across the 6 of their papers we have counts for
6 papers
Subsampling MCMC for Bayesian Variable Selection and Model Averaging in BGNLM
Jon Lachmann, Aliaksandr Hubin
Bayesian Generalized Nonlinear Models (BGNLM) offer a flexible nonlinear alternative to GLM while still providing better interpretability than machine learning techniques such as n…
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…
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…
Rejoinder for the discussion of the paper "A novel algorithmic approach to Bayesian Logic Regression"
Aliaksandr Hubin, Geir Storvik, Florian Frommlet
In this rejoinder we summarize the comments, questions and remarks on the paper "A novel algorithmic approach to Bayesian Logic Regression" from the discussants. We then respond to…
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…