1.7k citations · 4.6k across the 36 of their papers we have counts for
24 papers · 1 filter
Bayesian cross-validation by parallel Markov Chain Monte Carlo
Alex Cooper, Aki Vehtari, Catherine Forbes +2
Brute force cross-validation (CV) is a method for predictive assessment and model selection that is general and applicable to a wide range of Bayesian models. Naive or `brute force…
Cross-validatory model selection for Bayesian autoregressions with exogenous regressors
Alex Cooper, Dan Simpson, Lauren Kennedy +2
Bayesian cross-validation (CV) is a popular method for predictive model assessment that is simple to implement and broadly applicable. A wide range of CV schemes is available for t…
Treatment effect estimation with Multilevel Regression and Poststratification
Yuxiang Gao, Lauren Kennedy, Daniel Simpson
Multilevel regression and poststratification (MRP) is a flexible modeling technique that has been used in a broad range of small-area estimation problems. Traditionally, MRP studie…
Bayesian Workflow
Andrew Gelman, Aki Vehtari, Daniel Simpson +7
The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory. Probabilis…
Improving multilevel regression and poststratification with structured priors
Yuxiang Gao, Lauren Kennedy, Daniel Simpson +1
A central theme in the field of survey statistics is estimating population-level quantities through data coming from potentially non-representative samples of the population. Multi…
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.