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Found 5 papers, 3 papers with code
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.
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
