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Bayesian Models : A Statistical Primer for Ecologists, 2nd Edition by N. Thompson Hobbs and Mevin B. Hooten (2025, Hardcover)
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About this product
Product Identifiers
PublisherPrinceton University Press
ISBN-10069125012X
ISBN-139780691250120
eBay Product ID (ePID)15070486654
Product Key Features
Number of Pages360 Pages
Publication NameBayesian Models : a Statistical Primer for Ecologists, 2nd Edition
LanguageEnglish
SubjectLife Sciences / Ecology, Life Sciences / Biology, Probability & Statistics / Bayesian Analysis
Publication Year2025
TypeTextbook
AuthorN. Thompson Hobbs, Mevin B. Hooten
Subject AreaMathematics, Science
FormatHardcover
Dimensions
Item Height1.2 in
Item Weight25.9 Oz
Item Length9.5 in
Item Width6.4 in
Additional Product Features
Intended AudienceCollege Audience
LCCN2024-057943
Dewey Edition23
IllustratedYes
Dewey Decimal577.015195
SynopsisA fully updated and expanded edition of the essential primer on Bayesian modeling for ecologists Uniquely suited to deal with complexity in a statistically coherent way, Bayesian modeling has become an indispensable tool for ecological research. This book teaches the basic principles of mathematics and statistics needed to apply Bayesian models to the analysis of ecological data, using language non-statisticians can understand. Deemphasizing computer coding in favor of a clear treatment of model building, it starts with a definition of probability and proceeds step-by-step through distribution theory, likelihood, simple Bayesian models, and hierarchical Bayesian models. Now revised and expanded, Bayesian Models enables students and practitioners to gain new insights from ecological models and data properly tempered by uncertainty. Covers the basic rules of probability needed to model diverse types of ecological data in the Bayesian framework Shows how to write proper mathematical expressions for posterior distributions using directed acyclic graphs as templates Explains how to use the powerful Markov chain Monte Carlo algorithm to find posterior distributions of model parameters, latent states, and missing data Teaches how to check models to assure they meet the assumptions of model-based inference Demonstrates how to make inferences from single and multiple Bayesian models Provides worked problems for practicing and strengthening modeling skills Features new chapters on spatial models and modeling missing data, Uniquely suited to deal with complexity in a statistically coherent way, Bayesian modeling has become an indispensable tool for ecological research. This book teaches the basic principles of mathematics and statistics needed to apply Bayesian models to the analysis of ecological data, using language non-statisticians can understand. Deemphasizing computer coding in favor of a clear treatment of model building, it starts with a definition of probability and proceeds step-by-step through distribution theory, likelihood, simple Bayesian models, and hierarchical Bayesian models. Now revised and expanded, Bayesian Models enables students and practitioners to gain new insights from ecological models and data properly tempered by uncertainty. . Covers the basic rules of probability needed to model diverse types of ecological data in the Bayesian framework . Shows how to write proper mathematical expressions for posterior distributions using directed acyclic graphs as templates . Explains how to use the powerful Markov chain Monte Carlo algorithm to find posterior distributions of model parameters, latent states, and missing data . Teaches how to check models to assure they meet the assumptions of model-based inference . Demonstrates how to make inferences from single and multiple Bayesian models . Provides worked problems for practicing and strengthening modeling skills . Features new chapters on spatial models and modeling missing data
LC Classification NumberQH541.15.S72H63 2025
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