5.7k citations · 12k across the 78 of their papers we have counts for
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Limitations of "Limitations of Bayesian leave-one-out cross-validation for model selection"
Aki Vehtari, Daniel P. Simpson, Yuling Yao +1
This article is an invited discussion of the article by Gronau and Wagenmakers (2018) that can be found at https://dx.doi.org/10.1007/s42113-018-0011-7.
Projective Inference in High-dimensional Problems: Prediction and Feature Selection
Juho Piironen, Markus Paasiniemi, Aki Vehtari
This paper discusses predictive inference and feature selection for generalized linear models with scarce but high-dimensional data. We argue that in many cases one can benefit fro…
Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models
Paul-Christian Bürkner, Jonah Gabry, Aki Vehtari
Cross-validation can be used to measure a model's predictive accuracy for the purpose of model comparison, averaging, or selection. Standard leave-one-out cross-validation (LOO-CV)…
Validating Bayesian Inference Algorithms with Simulation-Based Calibration
Sean Talts, Michael Betancourt, Daniel Simpson +2
Verifying the correctness of Bayesian computation is challenging. This is especially true for complex models that are common in practice, as these require sophisticated model imple…
Yes, but Did It Work?: Evaluating Variational Inference
Yuling Yao, Aki Vehtari, Daniel Simpson +1
While it's always possible to compute a variational approximation to a posterior distribution, it can be difficult to discover problems with this approximation. We propose two diag…
Bayesian Estimation of Gaussian Graphical Models with Predictive Covariance Selection
Donald R. Williams, Juho Piironen, Aki Vehtari +1
Gaussian graphical models are used for determining conditional relationships between variables. This is accomplished by identifying off-diagonal elements in the inverse-covariance…