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Bayesian ModelsA Statistical Primer for Ecologists$
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N. Thompson Hobbs and Mevin B. Hooten

Print publication date: 2015

Print ISBN-13: 9780691159287

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691159287.001.0001

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Simple Bayesian Models

Simple Bayesian Models

Chapter:
(p.79) 5 Simple Bayesian Models
Source:
Bayesian Models
Author(s):

N. Thompson Hobbs

Mevin B. Hooten

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691159287.003.0005

This chapter lays out the basic principles of Bayesian inference, building on the concepts of probability developed in Chapter 3. It seeks to use the rules of probability to show how Bayes' theorem works, by making use of the conditional rule of probability and the law of total probability. The chapter begins with the central, underpinning tenet of the Bayesian view: the world can be divided into quantities that are observed and quantities that are unobserved. Unobserved quantities include parameters in models, latent states predicted by models, missing data, effect sizes, future states, and data before they are observed. We wish to learn about these quantities using observations. The Bayesian framework for achieving that understanding is applied in exactly the same way regardless of the specifics of the research problem at hand or the nature of the unobserved quantities.

Keywords:   Bayesian inference, probability, Bayes' theorem, probability, observed quantities, unobserved quantities, observations, simple Bayesian models, likelihood

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