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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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Bayesian Models
Author(s):

N. Thompson Hobbs

Mevin B. Hooten

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691159287.003.0001

This chapter sketches an approach to inference applicable to an enormous range of research problems—one that can be understood from first principles and that can be unambiguously communicated to other scientists, managers, and policy makers. In doing research, it is important that one is able to ask important questions and provide compelling answers to them. Doing so depends on establishing a line of inference that extends from current thinking, theory, and questions to new insight qualified by uncertainty. This chapter introduces a highly general, flexible approach to establishing this line of inference. It offers a somewhat abstract overview of this framework followed by a concrete example to properly illustrate this framework.

Keywords:   research problems, inference, ecology, Bayesian model, hierarchical models, ecological systems

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