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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 a Single Model

Inference from a Single Model

Chapter:
(p.181) 8 Inference from a Single Model
Source:
Bayesian Models
Author(s):

N. Thompson Hobbs

Mevin B. Hooten

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691159287.003.0008

This chapter shows how to make inferences using MCMC samples. Here, the process of inference begins on the assumption that a single model is being analyzed. The objective is to estimate parameters, latent states, and derived quantities based on that model and the data. These estimates are conditioned on the single model being analyzed. The chapter also returns to an example advanced in the first chapter, to illustrate choices on specific distributions needed to implement the model, to show how informative priors can be useful, and to illustrate some of the inferential procedures described in this chapter—posterior predictive checks, marginal posterior distributions, estimates of derived quantities, and forecasting.

Keywords:   MCMC samples, inferences, single model, model checking, marginal posterior distributions, derived quantities, unobserved quantities

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