Open Access
February 2017 Leave Pima Indians Alone: Binary Regression as a Benchmark for Bayesian Computation
Nicolas Chopin, James Ridgway
Statist. Sci. 32(1): 64-87 (February 2017). DOI: 10.1214/16-STS581


Whenever a new approach to perform Bayesian computation is introduced, a common practice is to showcase this approach on a binary regression model and datasets of moderate size. This paper discusses to which extent this practice is sound. It also reviews the current state of the art of Bayesian computation, using binary regression as a running example. Both sampling-based algorithms (importance sampling, MCMC and SMC) and fast approximations (Laplace, VB and EP) are covered. Extensive numerical results are provided, and are used to make recommendations to both end users and Bayesian computation experts. Implications for other problems (variable selection) and other models are also discussed.


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Nicolas Chopin. James Ridgway. "Leave Pima Indians Alone: Binary Regression as a Benchmark for Bayesian Computation." Statist. Sci. 32 (1) 64 - 87, February 2017.


Published: February 2017
First available in Project Euclid: 6 April 2017

zbMATH: 06946264
MathSciNet: MR3634307
Digital Object Identifier: 10.1214/16-STS581

Keywords: Bayesian computation , expectation propagation , Markov chain Monte Carlo , sequential Monte Carlo , variational inference

Rights: Copyright © 2017 Institute of Mathematical Statistics

Vol.32 • No. 1 • February 2017
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