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BAYESIANANALYSISFORTHESOCIAL SCIENCES
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Generally, “Bayesian analysis” refers to the statistical analysis of data that relies on
Bayes’ Theorem, presented below. Bayes’ Theorem tells us how to update prior beliefs
about parameters or hypotheses in light of data, to yield posterior beliefs. Or, even more
simply, Bayes’ Theorem tells us how to learn rationally about parameters from data. As
we shall see, Bayesian analysis is often more easily said than done, or at least this was
the case up until recently. In the 1990s there was a veritable explosion of interest in
Bayesian analysis in the statistics profession, that has now crossed over into quantitative
social science. The mathematics and computation underlying Bayesian analysis has been
dramatically simplified via a suite of algorithms known collectively as Markov chain Monte
Carlo (MCMC), to be discussed in Chapter 4. The combination of the popularization of
MCMC and vast increases in the computing power available to social scientists means that
Bayesiananalysisisnowwellandtrulypartofthemainstreamofquantitativesocialscience,
as we will see in some detail in later chapters.
Simon Jackman - Personal Name
NONE
Social Science
English
November 2, 2005
1-163
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