5.7k citations · 20k across the 76 of their papers we have counts for
31 papers · 1 filter
Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity
Martin Modrák, Angie H. Moon, Shinyoung Kim +5
Simulation-based calibration checking (SBC) is a practical method to validate computationally-derived posterior distributions or their approximations. In this paper, we introduce a…
Nested : Assessing the convergence of Markov chain Monte Carlo when running many short chains
Charles C. Margossian, Matthew D. Hoffman, Pavel Sountsov +3
Recent developments in parallel Markov chain Monte Carlo (MCMC) algorithms allow us to run thousands of chains almost as quickly as a single chain, using hardware accelerators such…
Inference from Non-Random Samples Using Bayesian Machine Learning
Yutao Liu, Andrew Gelman, Qixuan Chen
We consider inference from non-random samples in data-rich settings where high-dimensional auxiliary information is available both in the sample and the target population, with sur…
Bayesian hierarchical stacking: Some models are (somewhere) useful
Yuling Yao, Gregor Pirš, Aki Vehtari +1
Stacking is a widely used model averaging technique that asymptotically yields optimal predictions among linear averages. We show that stacking is most effective when model predict…
What are the most important statistical ideas of the past 50 years?
Andrew Gelman, Aki Vehtari
We review the most important statistical ideas of the past half century, which we categorize as: counterfactual causal inference, bootstrapping and simulation-based inference, over…
A proposal for informative default priors scaled by the standard error of estimates
Erik van Zwet, Andrew Gelman
If we have an unbiased estimate of some parameter of interest, then its absolute value is positively biased for the absolute value of the parameter. This bias is large when the sig…
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…
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors
Yuling Yao, Aki Vehtari, Andrew Gelman
When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms have difficulty moving between modes, and default variational or mode-base…
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…
Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data
Andrew Gelman, Matthijs Vákár
It is not always clear how to adjust for control data in causal inference, balancing the goals of reducing bias and variance. We show how, in a setting with repeated experiments, B…
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.
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…
Bayesian Inference under Cluster Sampling with Probability Proportional to Size
Susanna Makela, Yajuan Si, Andrew Gelman
Cluster sampling is common in survey practice, and the corresponding inference has been predominantly design-based. We develop a Bayesian framework for cluster sampling and account…
Large Scale Replication Projects in Contemporary Psychological Research
Blakeley B. McShane, Jennifer L. Tackett, Ulf Bockenholt +1
Replication is complicated in psychological research because studies of a given psychological phenomenon can never be direct or exact replications of one another, and thus effect s…
Abandon Statistical Significance
Blakeley B. McShane, David Gal, Andrew Gelman +2
We discuss problems the null hypothesis significance testing (NHST) paradigm poses for replication and more broadly in the biomedical and social sciences as well as how these probl…
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…
The prior can generally only be understood in the context of the likelihood
Andrew Gelman, Daniel Simpson, Michael Betancourt
A key sticking point of Bayesian analysis is the choice of prior distribution, and there is a vast literature on potential defaults including uniform priors, Jeffreys' priors, refe…
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…
Using stacking to average Bayesian predictive distributions
Yuling Yao, Aki Vehtari, Daniel Simpson +1
The widely recommended procedure of Bayesian model averaging is flawed in the M-open setting in which the true data-generating process is not one of the candidate models being fit.…
The statistical significance filter leads to overconfident expectations of replicability
Shravan Vasishth, Andrew Gelman
We show that publishing results using the statistical significance filter---publishing only when the p-value is less than 0.05---leads to a vicious cycle of overoptimistic expectat…
How Bayesian Analysis Cracked the Red-State, Blue-State Problem
Andrew Gelman
In the United States as in other countries, political and economic divisions cut along geographic and demographic lines. Richer people are more likely to vote for Republican candid…
Bayesian Nonparametric Weighted Sampling Inference
Yajuan Si, Natesh S. Pillai, Andrew Gelman
It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference in the prese…
Convincing Evidence
Andrew Gelman, Keith O'Rourke
Textbooks on statistics emphasize care and precision, via concepts such as reliability and validity in measurement, random sampling and treatment assignment in data collection, and…
Understanding predictive information criteria for Bayesian models
Andrew Gelman, Jessica Hwang, Aki Vehtari
We review the Akaike, deviance, and Watanabe-Akaike information criteria from a Bayesian perspective, where the goal is to estimate expected out-of-sample-prediction error using a…