book chapter · Oxford University Press eBooks · 2009
Bayesian Analysis
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Abstract
Abstract 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.
Cite this paper
Martin, A. D. (2009). Bayesian Analysis. Oxford University Press eBooks, 494–510. https://doi.org/10.1093/oxfordhb/9780199286546.003.0021
Martin, Andrew D. “Bayesian Analysis.” Oxford University Press eBooks, 2009, pp. 494–510, https://doi.org/10.1093/oxfordhb/9780199286546.003.0021.
Martin, Andrew D. 2009. “Bayesian Analysis.” Oxford University Press eBooks: 494–510. https://doi.org/10.1093/oxfordhb/9780199286546.003.0021.
Martin, A.D. (2009) ‘Bayesian Analysis’, Oxford University Press eBooks, pp. 494–510. Available at: https://doi.org/10.1093/oxfordhb/9780199286546.003.0021.
@incollection{martin2009bayesian,
title = {{Bayesian Analysis}},
author = {Martin, Andrew D.},
booktitle = {Oxford University Press eBooks},
pages = {494--510},
year = {2009},
doi = {10.1093/oxfordhb/9780199286546.003.0021}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.