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Found 10 papers, 8 papers with code
Sparsifying Bayesian neural networks with latent binary variables and normalizing flows
In this paper, we will consider two extensions to the LBBNN method: Firstly, by using the local reparametrization trick (LRT) to sample the hidden units directly, we get a more computationally efficient algorithm.
Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty
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 inference techniques.
A sequential Monte Carlo approach to estimate a time varying reproduction number in infectious disease models: the Covid-19 case
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 transmission of the virus in the population.
Reversible Genetically Modified Mode Jumping MCMC
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 in complex model spaces, where the number of explanatory variables is prohibitively large for classical Markov Chain Monte Carlo methods.
Rejoinder for the discussion of the paper "A novel algorithmic approach to Bayesian Logic Regression"
In this rejoinder we summarize the comments, questions and remarks on the paper "A novel algorithmic approach to Bayesian Logic Regression" from the discussants.
Flexible Bayesian Nonlinear Model Configuration
In this paper, we introduce a flexible approach for the construction and selection of highly flexible nonlinear parametric regression models.
Combining Model and Parameter Uncertainty in Bayesian Neural Networks
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 inference techniques.
Deep Bayesian regression models
DBRM can easily be extended to include latent Gaussian variables to model complex correlation structures between observations, which seems to be not easily possible with existing deep learning approaches.
Methodology 62-02, 62-09, 62F07, 62F15, 62J12, 62J05, 62J99, 62M05, 05A16, 60J22, 92D20, 90C27, 90C59 G.1.2; G.1.6; G.2.1; G.3; I.2.0; I.2.6; I.2.8; I.5.1; I.6; I.6.4
A novel algorithmic approach to Bayesian Logic Regression
Logic regression was developed more than a decade ago as a tool to construct predictors from Boolean combinations of binary covariates.
Computation 62-02, 62-09, 62F07, 62F15, 62J12, 62J05, 62J99, 62M05, 05A16, 60J22, 92D20, 90C27, 90C59
Mode jumping MCMC for Bayesian variable selection in GLMM
An increasing number of sources of data are becoming available, introducing a variety of candidate explanatory variables for these models.
Computation 62-02, 62-09, 62F07, 62F15, 62J12, 62J05, 62J99, 62M05, 05A16, 60J22, 92D20, 90C27, 90C59
