5.7k citations · 7.4k across the 9 of their papers we have counts for
9 papers
Multilevel Regression and Poststratification Interface: An Application to Track Community-level COVID-19 Viral Transmission
Yajuan Si, Toan Tran, Jonah Gabry +2
We present a novel Bayesian workflow for multilevel regression and poststratification (MRP), introducing extensions to time-varying data and granular geography and publicly availab…
Fast methods for posterior inference of two-group normal-normal models
Philip Greengard, Jeremy Hoskins, Charles C. Margossian +2
We describe a class of algorithms for evaluating posterior moments of certain Bayesian linear regression models with a normal likelihood and a normal prior on the regression coeffi…
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…
Approximate leave-future-out cross-validation for Bayesian time series models
Paul-Christian Bürkner, Jonah Gabry, Aki Vehtari
One of the common goals of time series analysis is to use the observed series to inform predictions for future observations. In the absence of any actual new data to predict, cross…
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)…
Visualization in Bayesian workflow
Jonah Gabry, Daniel Simpson, Aki Vehtari +2
Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian da…
Bayesian hierarchical weighting adjustment and survey inference
Yajuan Si, Rob Trangucci, Jonah Sol Gabry +1
We combine Bayesian prediction and weighted inference as a unified approach to survey inference. The general principles of Bayesian analysis imply that models for survey outcomes s…
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…