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Found 7 papers, 5 papers with code
A Decision-Theoretic Approach for Model Interpretability in Bayesian Framework
Through experiments on real-word data sets, using decision trees as interpretable models and Bayesian additive regression models as reference models, we show that for the same level of interpretability, our approach generates more accurate models than the alternative of restricting the prior.
Implicitly Adaptive Importance Sampling
Adaptive importance sampling is a class of techniques for finding good proposal distributions for importance sampling.
Projective Inference in High-dimensional Problems: Prediction and Feature Selection
This paper discusses predictive inference and feature selection for generalized linear models with scarce but high-dimensional data.
Bayesian Estimation of Gaussian Graphical Models with Projection Predictive Selection
Here we introduce a Bayesian method for estimating sparse matrices, in which conditional relationships are determined with projection predictive selection.
Applications Methodology
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution
Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse length-scale parameter of each input variable as a proxy for variable relevance.
Iterative Supervised Principal Components
In high-dimensional prediction problems, where the number of features may greatly exceed the number of training instances, fully Bayesian approach with a sparsifying prior is known to produce good results but is computationally challenging.
Methodology
Comparison of Bayesian predictive methods for model selection
From a predictive viewpoint, best results are obtained by accounting for model uncertainty by forming the full encompassing model, such as the Bayesian model averaging solution over the candidate models.
