Bayesian Analysis

Andrew D. Martin

in The Oxford Handbook of Political Methodology

Published in print August 2008 | ISBN: 9780199286546
Published online September 2009 | e-ISBN: 9780191577307 | DOI:

Series: Oxford Handbooks of Political Science

Bayesian Analysis

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This article surveys modern Bayesian methods of estimating statistical models. It first provides an introduction to the Bayesian approach for statistical inference, contrasting it with more conventional approaches. It then explains the Monte Carlo principle and reviews commonly used Markov Chain Monte Carlo (MCMC) methods. This is followed by a practical justification for the use of Bayesian methods in the social sciences, and a number of examples from the literature where Bayesian methods have proven useful are shown. The article finally provides a review of modern software for Bayesian inference, and a discussion of the future of Bayesian methods in political science. One area ripe for research is the use of prior information in statistical analyses. Mixture models and those with discrete parameters (such as change point models in the time-series context) are completely underutilized in political science.

Keywords: Bayesian method; Markov Chain Monte Carlo methods; software; statistical models; social sciences; political science

Article.  6871 words. 

Subjects: Political Methodology ; Comparative Politics

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