Bayesian posterior distributions without Markov chains

Stephen R. Cole, Haitao Chu, Sander Greenland, Ghassan Hamra, David B. Richardson

Research output: Contribution to journalArticle

Abstract

Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976-1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984-1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC.

Original languageEnglish (US)
Pages (from-to)368-375
Number of pages8
JournalAmerican Journal of Epidemiology
Volume175
Issue number5
DOIs
Publication statusPublished - Mar 1 2012
Externally publishedYes

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Keywords

  • Bayes theorem
  • epidemiologic methods
  • inference
  • Monte Carlo method
  • posterior distribution
  • simulation

ASJC Scopus subject areas

  • Epidemiology

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