The Resource Bayesian models : a statistical primer for ecologists, N. Thompson Hobbs and Mevin B. Hooten
Bayesian models : a statistical primer for ecologists, N. Thompson Hobbs and Mevin B. Hooten
Resource Information
The item Bayesian models : a statistical primer for ecologists, N. Thompson Hobbs and Mevin B. Hooten represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of San Diego Libraries.This item is available to borrow from 1 library branch.
Resource Information
The item Bayesian models : a statistical primer for ecologists, N. Thompson Hobbs and Mevin B. Hooten represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of San Diego Libraries.
This item is available to borrow from 1 library branch.
 Summary
 "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 methodsin language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a bigpicture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for nonstatisticians. It begins with a definition of probability and develops a stepbystep 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. Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to nonstatisticians Covers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more Deemphasizes computer coding in favor of basic principles Explains how to write out properly factored statistical expressions representing Bayesian models."
 Language
 eng
 Extent
 1 online resource (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
 Isbn
 9781400866557
 Label
 Bayesian models : a statistical primer for ecologists
 Title
 Bayesian models
 Title remainder
 a statistical primer for ecologists
 Statement of responsibility
 N. Thompson Hobbs and Mevin B. Hooten
 Language
 eng
 Summary
 "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 methodsin language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a bigpicture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for nonstatisticians. It begins with a definition of probability and develops a stepbystep 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. Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to nonstatisticians Covers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more Deemphasizes computer coding in favor of basic principles Explains how to write out properly factored statistical expressions representing Bayesian models."
 Cataloging source
 AU@
 http://library.link/vocab/creatorName
 Hobbs, N. Thompson
 Index
 index present
 Language note
 In English
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 http://library.link/vocab/relatedWorkOrContributorDate
 1976
 http://library.link/vocab/relatedWorkOrContributorName
 Hooten, Mevin B.
 http://library.link/vocab/subjectName

 Ecology
 Bayesian statistical decision theory
 MATHEMATICS
 MATHEMATICS
 SCIENCE
 Bayesian statistical decision theory
 Ecology
 Label
 Bayesian models : a statistical primer for ecologists, N. Thompson Hobbs and Mevin B. Hooten
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 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
 Control code
 ocn921131921
 Dimensions
 unknown
 Extent
 1 online resource (xiv, 300 pages)
 Form of item
 online
 Isbn
 9781400866557
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Note
 JSTOR
 Other control number
 10.1515/9781400866557
 http://library.link/vocab/ext/overdrive/overdriveId

 803585
 22573/ctt1dr30zr
 Specific material designation
 remote
 System control number
 (OCoLC)921131921
 Label
 Bayesian models : a statistical primer for ecologists, N. Thompson Hobbs and Mevin B. Hooten
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 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
 Control code
 ocn921131921
 Dimensions
 unknown
 Extent
 1 online resource (xiv, 300 pages)
 Form of item
 online
 Isbn
 9781400866557
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Note
 JSTOR
 Other control number
 10.1515/9781400866557
 http://library.link/vocab/ext/overdrive/overdriveId

 803585
 22573/ctt1dr30zr
 Specific material designation
 remote
 System control number
 (OCoLC)921131921
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.sandiego.edu/portal/Bayesianmodelsastatisticalprimerfor/BrYvEYlUhdw/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.sandiego.edu/portal/Bayesianmodelsastatisticalprimerfor/BrYvEYlUhdw/">Bayesian models : a statistical primer for ecologists, N. Thompson Hobbs and Mevin B. Hooten</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.sandiego.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.sandiego.edu/">University of San Diego Libraries</a></span></span></span></span></div>