Introduction to Hierarchical Bayesian Modeling for Ecological Data

ISBN-13: 9781584889199 / Angielski / Twarda / 2012 / 427 str.

Eric Parent

Opis

Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models. The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors website. This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction."

Spis treści

I Basic Blocks of Bayesian Modeling: Bayesian Hierarchical Models in Statistical Ecology. The Beta-Binomial Model. The Basic Normal Model. Working with More Than One Beta-Binomial Element. Combining Various Sources of Information. The Normal Linear Model. Nonlinear Models for Stock-Recruitment Analysis. Getting beyond Regression Models. II More Elaborate Hierarchical Structures: HBM I: Borrowing Strength from Similar Units. HBM II: Piling up Simple Layers. HBM III: State-Space Modeling. Decision and Planning. Appendices. Bibliography. Index.