Introduction to hierarchical Bayesian modeling for ecological data. (©2013)
- Record Type:
- Book
- Title:
- Introduction to hierarchical Bayesian modeling for ecological data. (©2013)
- Main Title:
- Introduction to hierarchical Bayesian modeling for ecological data
- Further Information:
- Note: Eric Parent, Etienne Rivot.
- Other Names:
- Parent, E (Eric), 1957-
Rivot, Etienne, 1974- - Contents:
- I Basic Blocks of Bayesian Modeling; Bayesian Hierarchical Models in Statistical Ecology ; Challenges for statistical ecology; Conditional reasoning, graphs and hierarchical models; Bayesian inferences on hierarchical models; What can be found in this book? The Beta-Binomial Model ; From a scientific question to a Bayesian analysis; What is modeling?; Think conditionally and make a graphical representation; Inference is the reverse way of thinking; Expertise matters; Encoding prior knowledge; The conjugate Beta pdf; Bayesian inference as statistical learning; Bayesian inference as a statistical tool for prediction; Asymptotic behavior of the beta-binomial model; The beta-binomial model with WinBUGS; Further references The Basic Normal Model ; Salmon farm’s pollutants and juvenile growth; A Normal model for the fish length; Normal-gamma as conjugate models to encode expertise; Inference by recourse to conjugate property; Bibliographical notes; Further material Working with More Than One Beta-Binomial Element ; Capture-mark-recapture analysis; Successive removal analysis; Testing a new tag for tuna; Further references Combining Various Sources of Information; Motivating example; Stochastic model for salmon behavior; Inference with WinBUGS; Results; Discussion and conclusions The Normal Linear Model ; The decrease of Thiof abundance in Senegal; Linear model theory; A linear model for Thiof abundance; Further reading Nonlinear Models for Stock-Recruitment Analysis ;I Basic Blocks of Bayesian Modeling; Bayesian Hierarchical Models in Statistical Ecology ; Challenges for statistical ecology; Conditional reasoning, graphs and hierarchical models; Bayesian inferences on hierarchical models; What can be found in this book? The Beta-Binomial Model ; From a scientific question to a Bayesian analysis; What is modeling?; Think conditionally and make a graphical representation; Inference is the reverse way of thinking; Expertise matters; Encoding prior knowledge; The conjugate Beta pdf; Bayesian inference as statistical learning; Bayesian inference as a statistical tool for prediction; Asymptotic behavior of the beta-binomial model; The beta-binomial model with WinBUGS; Further references The Basic Normal Model ; Salmon farm’s pollutants and juvenile growth; A Normal model for the fish length; Normal-gamma as conjugate models to encode expertise; Inference by recourse to conjugate property; Bibliographical notes; Further material Working with More Than One Beta-Binomial Element ; Capture-mark-recapture analysis; Successive removal analysis; Testing a new tag for tuna; Further references Combining Various Sources of Information; Motivating example; Stochastic model for salmon behavior; Inference with WinBUGS; Results; Discussion and conclusions The Normal Linear Model ; The decrease of Thiof abundance in Senegal; Linear model theory; A linear model for Thiof abundance; Further reading Nonlinear Models for Stock-Recruitment Analysis ; Stock-recruitment motivating example; Searching for a SR model; Which parameters?; Changing the error term from lognormal to gamma; From Ricker to Beverton and Holt; Model choice with informative prior; Conclusions and perspectives Getting beyond Regression Models ; Logistic and probit regressions; Ordered probit model; Discussion II More Elaborate Hierarchical Structures; HBM I : Borrowing Strength from Similar Units ; Introduction; HBM for capture-mark-recapture data; Hierarchical stock-recruitment analysis; Further Bayesian comments on exchangeability HBM II : Piling up Simple Layers ; HBM for successive removal data with habitat and year; Electrofishing with successive removals HBM III : State-Space Modeling; Introduction; State-space modeling of a biomass production model; State-space modeling of Atlantic salmon life cycle model; A tool of choice for the ecological detective Decision and Planning; Summary; Introduction; The Sée-Sélune river network; Salmon life cycle dynamics; Long-term behavior: Collapse or equilibrium?; Management reference points; Management rules and implementation error; Economic model; Results; Discussion Appendix A: The Normal and Linear Normal Model; Appendix B: Computing Marginal Likelihoods; Appendix C: The Baseball Players’ Historical Example; Appendix D: More on Ricker Stock-Recruitment Bibliography Index … (more)
- Publisher Details:
- Boca Raton, FL : Taylor & Francis
- Publication Date:
- 2013
- Copyright Date:
- 2013
- Extent:
- 1 online resource (xxi, 402 pages), illustrations
- Subjects:
- 577.072/7
Ecology -- Statistical methods
Bayesian statistical decision theory
Ecology
Bayes Theorem
NATURE -- Ecology
NATURE -- Ecosystems & Habitats -- Wilderness
SCIENCE -- Environmental Science
SCIENCE -- Life Sciences -- Ecology
Bayesian statistical decision theory
Ecology -- Statistical methods
Electronic books - Languages:
- English
- ISBNs:
- 9781584889205
1584889209
1299992382
9781299992382 - Related ISBNs:
- 9781584889199
1584889195 - Notes:
- Note: Includes bibliographical references (pages 375-402) and index.
Note: Print version record. - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.143715
- Ingest File:
- 01_045.xml