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2013•Oxford University Press eBooksRequires access

Bayesian Statistical Methods

David Kaplan, Sarah Depaoli

Open publisher page 45 citations

Abstract

This chapter provides a general overview of Bayesian statistical methods. Topics include the notion of probability from a Bayesian perspective, Bayesian inference and hypothesis testing, and Bayesian computation. Three examples are provided to demonstrate the utility of Bayesian methods: simple linear regression, multilevel regression, and confirmatory factor analysis. Throughout the chapter, references are made to the epistemological differences between Bayesian theory and classical (frequentist) theory.

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What this paper is about

This chapter provides a general overview of Bayesian statistical methods. Topics include the notion of probability from a Bayesian perspective, Bayesian inference and hypothesis testing, and Bayesian computation. Three examples are provided to demonstrate the utility of Bayesian methods: simple linear regression, multilevel regression, and confirmatory factor analysis. Throughout the chapter, references are made to the epistemological differences between Bayesian theory and classical (frequentist) theory.

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Available abstract

This chapter provides a general overview of Bayesian statistical methods. Topics include the notion of probability from a Bayesian perspective, Bayesian inference and hypothesis testing, and Bayesian computation. Three examples are provided to demonstrate the utility of Bayesian methods: simple linear regression, multilevel regression, and confirmatory factor analysis. Throughout the chapter, references are made to the epistemological differences between Bayesian theory and classical (frequentist) theory.

Key concepts: Frequentist inference, Bayesian probability, Bayesian statistics, Bayesian linear regression, Bayesian average, Bayesian econometrics, Statistical inference, Bayes factor

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