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Description: |
xiv, 299 pages : illustrations ; 25 cm |
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Bibliography Note: |
Includes bibliographical references (pages 283-291) and index. |
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Summary, Etc. Note: |
Summary: 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.--Publisher description. |
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Contents Note: |
Contents: Machine generated contents note: I. Fundamentals -- 1. Preview -- 1.1. Line of Inference for Ecology -- 1.2. Example Hierarchical Model -- 1.3. What Lies Ahead? -- 2. Deterministic Models -- 2.1. Modeling Styles in Ecology -- 2.2. 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. Use of Prior Information in Maximum Likelihood -- 5. Simple Bayesian Models -- 5.1. Bayes' Theorem -- 5.2. 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 Quantities -- 8.5. 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. General Approach -- 10.2. 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 Ticks -- 12.2. 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. |
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Name Added Entry: |
Hooten, Mevin B., 1976- author. |