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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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Hierarchical Bayesian Models

Hierarchical Bayesian Models

Chapter:
(p.107) 6 Hierarchical Bayesian Models
Source:
Bayesian Models
Author(s):

N. Thompson Hobbs

Mevin B. Hooten

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691159287.003.0006

This chapter seeks to explain hierarchical models and how they differ from simple Bayesian models and to illustrate building hierarchical models using mathematically correct expressions. It begins with the definition of hierarchical models. Next, the chapter introduces four general classes of hierarchical models that have broad application in ecology. These classes can be used individually or in combination to attack virtually any research problem. Examples are used to show how to draw Bayesian networks that portray stochastic relationships between observed and unobserved quantities. The chapter furthermore shows how to use network drawings as a guide for writing posterior and joint distributions.

Keywords:   hierarchical models, hierarchical Bayesian models, Bayesian networks, stochastic relationships, network drawings, posterior distribution, joint distribution

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