Titlebook: Introduction to Bayesian Methods in Ecology and Natural Resources; Edwin J. Green,Andrew O. Finley,William E. Strawde Textbook 2020 Spring

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Introduction to Bayesian Methods in Ecology and Natural Resources978-3-030-60750-0

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Edwin J. Green,Andrew O. Finley,William E. Strawdermanion of fracture mechanics, but it was aimed to treat the subject in a way that may interest both metallurgists and engineers. For the latter, some general knowledge of fracture mechanisms and fracture criteria is indispensable for an apprecia­ tion of the limita tions of fracture mechanics. Therefor

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Edwin J. Green,Andrew O. Finley,William E. Strawdermanion of fracture mechanics, but it was aimed to treat the subject in a way that may interest both metallurgists and engineers. For the latter, some general knowledge of fracture mechanisms and fracture criteria is indispensable for an apprecia­ tion of the limita tions of fracture mechanics. Therefor

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ensive worked examples of biological data analysis, using op.This book presents modern Bayesian analysis in a format that is accessible to researchers in the fields of ecology, wildlife biology, and natural resource management. Bayesian analysis has undergone a remarkable transformation since the ea

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Textbook 2020ource management. Bayesian analysis has undergone a remarkable transformation since the early 1990s. Widespread adoption of Markov chain Monte Carlo techniques has made the Bayesian paradigm the viable alternative to classical statistical procedures for scientific inference. The Bayesian approach ha

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Probability Theory and Some Useful Probability Distributions,ution) and the sampling distribution, also referred to as the data model, for the observable data. Hence in this chapter we will review some commonly used probability distributions. Readers already knowledgeable about probability distributions should skim this chapter to insure that they are comfortable with our notation and terminology.

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Hypothesis Testing and Model Choice,wants to select a single model. In this chapter we consider the situation in which a scientist would like to select one of the candidate models to use for inference. Model selection shares many concepts with hypothesis testing, and so we begin this chapter with a discussion of hypothesis testing.

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General Linear Models,r things, specifies the dependent variable is continuous. Or perhaps the dependent variable is a proportion, constrained to lie in the interval (0,1), e.g., the proportion of habitable land in a given area. The latter also fails the normality assumption, which specifies that the dependent variable is defined on the interval (-.).