Bayesian Models: A Statistical Primer for Ecologists

by N. Thompson Hobbs, Mevin B. Hooten

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods―in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.

Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.

This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.

Genres: Textbooks, Biology, Mathematics, Nonfiction

320 pages, Hardcover

First published July 27, 2015

Reviews

[Lillie · Rating 2 out of 5]

Well. I guess this is my first book of 2023. Did I learn something about Bayesian modeling? Yes. Did I understand all that I wanted to? No. There is a lot more statistical formulas and technical descriptions than I knew what to do with.

[Sarah · Rating 4 out of 5]

A good introduction and overview to the theory and application of Bayesian models

[Dirk Last · Rating 2 out of 5]

I'm finishing up my master's degree, which requires a strong understanding of Bayesian statistics, particularly in ecology. I've come to realize that my understanding of frequentist statistics may not be as strong as I initially thought, or perhaps it's just that the way this book presents its content isn't very intuitive to me. The numerous long and complex models scattered between dry paragraphs can be quite hard to follow, especially when there's very little context provided. I'm reading through the book a second time at my advisor's request, but I still can't say I'm a big fan. I've found better resources online and personally prefer the Springer book by Finley, Strawderman, and Green.

[F · Rating 3 out of 5]

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