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Bayesian population analysis using WinBUGS = a hierarchical perspective /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian population analysis using WinBUGS/ Marc Kery and Michael Schaub ; foreword by Steven R. Beissinger.
其他題名:	a hierarchical perspective /
作者:	K�ery, Marc.{me_controlnum}
其他作者:	Schaub, Michael.
出版者:	Waltham, MA :Academic Press, : 2012.,
面頁冊數:	1 online resource (xvii, 535 p.) :ill. (some col.)
內容註:	Preface Acknowledgements 1. Introduction 2. Very brief introduction to Bayesian statistical modeling 3. Introduction to the generalized linear model (GLM): The simplest model for count data 4. Introduction to random effects: The conventional Poisson GLMM for count data 5. State-space models 6. Estimation of population size 7. Estimation of survival probabilities using capture-recapture data 8. Estimation of survival probabilities using mark-recovery data 9. Multistate capture-recapture models 10. Estimation of survival and recruitment using the Jolly-Seber model 11. Integrated population models 12. Metapopulation modeling of abundance using hierarchical Poisson regression 13. Metapopulation modeling of species distributions using hierarchical logistic regression 14. Concluding remarks Appendices References.
標題:	Population biology - Data processing. -
電子資源:	http://www.sciencedirect.com/science/book/9780123870209
ISBN:	9780123870209 (electronic bk.)

Bayesian population analysis using WinBUGS = a hierarchical perspective /
K�ery, Marc.{me_controlnum}

Bayesian population analysis using WinBUGSa hierarchical perspective /[electronic resource] :Marc Kery and Michael Schaub ; foreword by Steven R. Beissinger. - 1st ed. - Waltham, MA :Academic Press,2012. - 1 online resource (xvii, 535 p.) :ill. (some col.)

Includes bibliographical references and index.

Preface Acknowledgements 1. Introduction 2. Very brief introduction to Bayesian statistical modeling 3. Introduction to the generalized linear model (GLM): The simplest model for count data 4. Introduction to random effects: The conventional Poisson GLMM for count data 5. State-space models 6. Estimation of population size 7. Estimation of survival probabilities using capture-recapture data 8. Estimation of survival probabilities using mark-recovery data 9. Multistate capture-recapture models 10. Estimation of survival and recruitment using the Jolly-Seber model 11. Integrated population models 12. Metapopulation modeling of abundance using hierarchical Poisson regression 13. Metapopulation modeling of species distributions using hierarchical logistic regression 14. Concluding remarks Appendices References.

Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS and its open-source sister OpenBugs is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. Comprehensive and richly-commented examples illustrate a wide range of models that are most relevant to the research of a modern population ecologist. All WinBUGS/OpenBUGS analyses are completely integrated in software R. Includes complete documentation of all R and WinBUGS code required to conduct analyses and shows all the necessary steps from having the data in a text file out of Excel to interpreting and processing the output from WinBUGS in R.

ISBN: 9780123870209 (electronic bk.)

Source: 1106587:10995121Elsevier Science & Technologyhttp://www.sciencedirect.comSubjects--Uniform Titles:

WinBUGS.
Subjects--Topical Terms:

1613465
Population biology
--Data processing.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: QH352 / .K47 2012

Dewey Class. No.: 577.8/80285

Bayesian population analysis using WinBUGS = a hierarchical perspective /
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