International Society for Bayesian Analysis

The International Society for Bayesian Analysis is a professional society founded to promote the development and application of Bayesian analysis useful in the solution of theoretical and applied problems in science, industry and government. By sponsoring and organizing meetings, publishing the electronic journal Bayesian Analysis, the quarterly ISBA Bulletin and other activities ISBA provides a focal point for those interested in Bayesian analysis and its applications.

Publications

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Constrained Bayesian Optimization with Noisy Experiments Benjamin Letham, Brian Karrer, Guilherme Ottoni, and Eytan Bakshy Bayesian Analysis (2018)
Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) Andrew Gelman Bayesian Analysis, Volume 1, Number 3 (2006)
Deep Learning: A Bayesian Perspective Nicholas G. Polson and Vadim Sokolov Bayesian Analysis, Volume 12, Number 4 (2017)
Prediction in $\mathcal{M}$ -complete Problems with Limited Sample Size Jennifer Lynn Clarke, Bertrand Clarke, and Chi-Wai Yu Bayesian Analysis, Volume 8, Number 3 (2013)
Using Stacking to Average Bayesian Predictive Distributions (with Discussion) Yuling Yao, Aki Vehtari, Daniel Simpson, and Andrew Gelman Bayesian Analysis, Volume 13, Number 3 (2018)