5.7k citations · 12k across the 78 of their papers we have counts for
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Projection predictive model selection for Gaussian processes
Juho Piironen, Aki Vehtari
We propose a new method for simplification of Gaussian process (GP) models by projecting the information contained in the full encompassing model and selecting a reduced number of…
Bayesian inference for spatio-temporal spike-and-slab priors
Michael Riis Andersen, Aki Vehtari, Ole Winther +1
In this work, we address the problem of solving a series of underdetermined linear inverse problems subject to a sparsity constraint. We generalize the spike-and-slab prior distrib…
Projection predictive variable selection using Stan+R
Juho Piironen, Aki Vehtari
This document is additional material to our previous study comparing several strategies for variable subset selection. Our recommended approach was to fit the full model with all t…
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
Aki Vehtari, Andrew Gelman, Jonah Gabry
Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bay…
Pareto Smoothed Importance Sampling
Aki Vehtari, Daniel Simpson, Andrew Gelman +2
Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be highly variable when the…
Comparison of Bayesian predictive methods for model selection
Juho Piironen, Aki Vehtari
The goal of this paper is to compare several widely used Bayesian model selection methods in practical model selection problems, highlight their differences and give recommendation…