FRM: An Intelligent Assistant for Financial Resource Management. - Andrew Gelman; Susan Altman; Matt Pallakoff; Ketan Doshi; Catherine Manago; Thomas C. Rindfleisch; Bruce G. Buchanan (AAAI, 1988)

Type S error rates for classical and Bayesian single and multiple comparison procedures. - Andrew Gelman; Francis Tuerlinckx (Comput. Stat., 2000)

Bayesian probabilistic extensions of a deterministic classification model. - Iwin Leenen; Iven Van Mechelen; Andrew Gelman (Comput. Stat., 2000)

An experimental study of storable votes. - Alessandra Casella; Andrew Gelman; Thomas R. Palfrey (Games Econ. Behav., 2006)

Multilevel (Hierarchical) Modeling: What It Can and Cannot Do. - Andrew Gelman (Technometrics, 2006)

Bayesian Measures of Explained Variance and Pooling in Multilevel (Hierarchical) Models. - Andrew Gelman; Iain Pardoe (Technometrics, 2006)

Manipulating and summarizing posterior simulations using random variable objects. - Jouni Kerman; Andrew Gelman (Stat. Comput., 2007)

Weighted Classical Variogram Estimation for Data With Clustering. - Cavan Reilly; Andrew Gelman (Technometrics, 2007)

Commentary - Discussion of the Article "Website Morphing". - Andrew Gelman (Mark. Sci., 2009)

In praise of the referee - Nicolas Chopin; Andrew Gelman; Kerrie L. Mengersen; Christian P. Robert (CoRR, 2012)

The Mythical Swing Voter. - David Rothschild; Sharad Goel; Andrew Gelman; Doug Rivers (CoRR, 2014)

The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. - Matthew D. Hoffman; Andrew Gelman (J. Mach. Learn. Res., 2014)

Understanding predictive information criteria for Bayesian models. - Andrew Gelman; Jessica Hwang; Aki Vehtari (Stat. Comput., 2014)

Automatic Variational Inference in Stan. - Alp Kucukelbir; Rajesh Ranganath; Andrew Gelman; David M. Blei (NIPS, 2015)

Simulation-efficient shortest probability intervals. - Ying Liu; Andrew Gelman; Tian Zheng (Stat. Comput., 2015)

Centralized Analysis of Local Data, with Dollars and Lives on the Line: Lessons from the Home Radon Experience. - Phillip N. Price; Andrew Gelman (Computational Social Science, 2016)

The statistical significance filter leads to overconfident expectations of replicability. - Shravan Vasishth; Andrew Gelman (CogSci, 2017)

A Safe Depth Forecasting Model for Insuring Tubewell Installations Against Arsenic Risk in Bangladesh. - Matilde Trevisani; Jie Shen; Alexander van Geen; Andrew Gelman; Shuky Ehrenberg; John Immel (ICCSA (5), 2017)

The Prior Can Often Only Be Understood in the Context of the Likelihood. - Andrew Gelman; Daniel Simpson; Michael Betancourt (Entropy, 2017)

Automatic Differentiation Variational Inference. - Alp Kucukelbir; Dustin Tran; Rajesh Ranganath; Andrew Gelman; David M. Blei (J. Mach. Learn. Res., 2017)

Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. - Aki Vehtari; Andrew Gelman; Jonah Gabry (Stat. Comput., 2017)

Erratum to: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. - Aki Vehtari; Andrew Gelman; Jonah Gabry (Stat. Comput., 2017)

Yes, but Did It Work?: Evaluating Variational Inference. - Yuling Yao; Aki Vehtari; Daniel Simpson; Andrew Gelman (ICML, 2018)

A Fast Linear Regression via SVD and Marginalization. - Philip Greengard; Andrew Gelman; Aki Vehtari (CoRR, 2020)

Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data. - Aki Vehtari; Andrew Gelman; Tuomas Sivula; Pasi Jylänki; Dustin Tran; Swupnil Sahai; Paul Blomstedt; John P. Cunningham; David Schiminovich; Christian P. Robert (J. Mach. Learn. Res., 2020)

Bayesian hierarchical stacking. - Yuling Yao; Gregor Pirs; Aki Vehtari; Andrew Gelman (CoRR, 2021)

To design interfaces for exploratory data analysis, we need theories of graphical inference. - Jessica Hullman; Andrew Gelman (CoRR, 2021)

Toward a Taxonomy of Trust for Probabilistic Machine Learning. - Tamara Broderick; Andrew Gelman; Rachael Meager; Anna L. Smith; Tian Zheng (CoRR, 2021)

Accounting for uncertainty during a pandemic. - Jon Zelner; Julien Riou; Ruth Etzioni; Andrew Gelman (Patterns, 2021)

Reviews. - Darren B. Glass; Béla Bajnok; Carolyn Yackel; Edray Goins; Jenna P. Carpenter; Stephen Kennedy; Jeanette Shakalli; Ursula Whitcher; Dave Kung; Kathryn Kozak; Robert Ghrist; Andrew Gelman (Am. Math. Mon., 2021)

The Worst of Both Worlds: A Comparative Analysis of Errors in Learning from Data in Psychology and Machine Learning. - Jessica Hullman; Sayash Kapoor; Priyanka Nanayakkara; Andrew Gelman; Arvind Narayanan (AIES, 2022)

Delivering data differently. - Gwynn Sturdevant; A. Jonathan R. Godfrey; Andrew Gelman (CoRR, 2022)

A fast regression via SVD and marginalization. - Philip Greengard; Andrew Gelman; Aki Vehtari (Comput. Stat., 2022)

Selecting on statistical significance and practical importance is wrong. - Blakeley B. McShane; Andrew Gelman (J. Inf. Technol., 2022)

Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors. - Yuling Yao; Aki Vehtari; Andrew Gelman (J. Mach. Learn. Res., 2022)

Pathfinder: Parallel quasi-Newton variational inference. - Lu Zhang; Bob Carpenter; Andrew Gelman; Aki Vehtari (J. Mach. Learn. Res., 2022)

Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. - Han Guo; Philip Greengard; Hongyi Wang 001; Andrew Gelman; Yoon Kim; Eric P. Xing (ICLR, 2023)

Artificial Intelligence and Aesthetic Judgment. - Jessica Hullman; Ari Holtzman; Andrew Gelman (CoRR, 2023)

Prediction scoring of data-driven discoveries for reproducible research. - Anna L. Smith; Tian Zheng; Andrew Gelman (Stat. Comput., 2023)

Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England. - Thomas Ward; Mitzi Morris; Andrew Gelman; Bob Carpenter; William Ferguson; Christopher E. Overton; Martyn Fyles (PLoS Comput. Biol., 2023)

Pareto Smoothed Importance Sampling. - Aki Vehtari; Daniel Simpson; Andrew Gelman; Yuling Yao; Jonah Gabry (J. Mach. Learn. Res., 2024)

Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models. - Judith A. Bouman; Anthony Hauser; Simon L. Grimm; Martin Wohlfender; Samir Bhatt; Elizaveta Semenova; Andrew Gelman; Christian L. Althaus; Julien Riou (PLoS Comput. Biol., 2024)

Statistics as a Social Activity: Attitudes toward Amalgamating Evidence. - Andrew Gelman; Keith O'Rourke (Entropy, 2024)

Visualizing distributions of covariance matrices. - Tomoki Tokuda; Ben Goodrich; Iven Van Mechelen; Andrew Gelman; Francis Tuerlinckx (J. Data Sci. Stat. Vis., 2025)

Conformal Prediction and Human Decision Making. - Jessica Hullman; Yifan Wu 005; Dawei Xie; Ziyang Guo; Andrew Gelman (CoRR, 2025)