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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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Problems

Problems

Chapter:
(p.243) 11 Problems
Source:
Bayesian Models
Author(s):

N. Thompson Hobbs

Mevin B. Hooten

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691159287.003.0011

This chapter provides a set of structured problems to hone the reader's skills in model building. Each problem requires the reader to draw a Bayesian network and write the posterior and joint distributions. Computational syntax is de-emphasized because there are many sources that teach the details of writing code for MCMC software using ecological examples and a few that show how to construct one's own MCMC algorithms. The examples offered in this chapter are drawn from several ecological sub-disciplines, though the intent of these problems is to be instructive regardless of the reader's focus of research. The chapter also offers some pointers for approaching these problems and how to apply their lessons to one's own research.

Keywords:   structured problems, model building, Bayesian network, posterior distributions, joint distributions, ecological sub-disciplines

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