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| Genre/Form: | e-books Livres numériques |
|---|---|
| Additional Physical Format: | Version imprimée : Hobbs, N. Thompson. Bayesian models. Princeton, New Jersey : Princeton University Press, [2015] |
| Material Type: | Document |
| Document Type: | Book, Computer File |
| All Authors / Contributors: |
N Thompson Hobbs; Mevin B Hooten |
| ISBN: | 9781400866557 1400866553 |
| OCLC Number: | 1032690985 |
| Description: | 1 ressource en ligne (xiv, 300 pages) |
| Contents: | Cover; Title; Copyright; Contents; Preface; I Fundamentals; 1 PREVIEW; 1.1 A Line of Inference for Ecology; 1.2 An Example Hierarchical Model; 1.3 What Lies Ahead?; 2 DETERMINISTIC MODELS; 2.1 Modeling Styles in Ecology; 2.2 A Few Good Functions; 3 PRINCIPLES OF PROBABILITY; 3.1 Why Bother with First Principles?; 3.2 Rules of Probability; 3.3 Factoring Joint Probabilities; 3.4 Probability Distributions; 4 LIKELIHOOD; 4.1 Likelihood Functions; 4.2 Likelihood Profiles; 4.3 Maximum Likelihood; 4.4 The Use of Prior Information in Maximum Likelihood; 5 SIMPLE BAYESIAN MODELS; 5.1 Bayes' Theorem. 5.2 The Relationship between Likelihood and Bayes'5.3 Finding the Posterior Distribution in Closed Form; 5.4 More about Prior Distributions; 6 HIERARCHICAL BAYESIAN MODELS; 6.1 What Is a Hierarchical Model?; 6.2 Example Hierarchical Models; 6.3 When Are Observation and Process Variance Identifiable?; II Implementation; 7 MARKOV CHAIN MONTE CARLO; 7.1 Overview; 7.2 How Does MCMC Work?; 7.3 Specifics of the MCMC Algorithm; 7.4 MCMC in Practice; 8 INFERENCE FROM A SINGLE MODEL; 8.1 Model Checking; 8.2 Marginal Posterior Distributions; 8.3 Derived Quantities. 8.4 Predictions of Unobserved Quantities8.5 Return to the Wildebeest; 9 INFERENCE FROM MULTIPLE MODELS; 9.1 Model Selection; 9.2 Model Probabilities and Model Averaging; 9.3 Which Method to Use?; III Practice in Model Building; 10 WRITING BAYESIAN MODELS; 10.1 A General Approach; 10.2 An Example of Model Building: Aboveground Net Primary Production in Sagebrush Steppe; 11 PROBLEMS; 11.1 Fisher's Ticks; 11.2 Light Limitation of Trees; 11.3 Landscape Occupancy of Swiss Breeding Birds; 11.4 Allometry of Savanna Trees; 11.5 Movement of Seals in the North Atlantic; 12 SOLUTIONS. 12.1 Fisher's Ticks12.2 Light Limitation of Trees; 12.3 Landscape Occupancy of Swiss Breeding Birds; 12.4 Allometry of Savanna Trees; 12.5 Movement of Seals in the North Atlantic; Afterword; Acknowledgments; A Probability Distributions and Conjugate Priors; Bibliography; Index. |
| Responsibility: | N. Thompson Hobbs and Mevin B. Hooten. |
Abstract:
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 probabili.
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