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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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Inference from Multiple Models

Inference from Multiple Models

Chapter:
(p.209) 9 Inference from Multiple Models
Source:
Bayesian Models
Author(s):

N. Thompson Hobbs

Mevin B. Hooten

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691159287.003.0009

This chapter describes how to evaluate alternative models with data. There are two broad ways to formally use multiple models: model selection and model averaging. In model selection, models are evaluated based on some criterion. In model averaging, inferences are made using all models in the set, or a subset of them, using a weighted average of posterior distributions of individual models. This chapter first discusses model selection before turning to model averaging in turn, before detailing some general considerations which might help the researcher's selection of a particular model, given that both methods have their own strengths and shortcomings.

Keywords:   alternative models, multiple models, model selection, model averaging, posterior distributions, model probabilities

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