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020 a| 9781032154237q| hardback;

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245 10 a| Bayesian analysis of capture-recapture data with hidden Markov modelsh| [electronic resource] :b| theory and case studies in R /c| Olivier Gimenez.;

250 a| First edition.;

260 a| Boca Raton FL :b| CRC Press,c| 2026.;

263 a| 2605;

300 a| pages cm.;

334 a| single unitb| mono0| http://id.loc.gov/vocabulary/issuance/mono;

353 a| bibliographyb| bibliography0| http://id.loc.gov/vocabulary/msupplcont/bibliography;

490 0 a| CRC interdisciplinary statistics series;

504 a| Includes bibliographical references.;

505 0 a| Bayesian statistics and MCMC -- NIMBLE tutorial -- Hidden Markov models -- Alive and dead -- Sites and states -- Dealing with covariates -- Addressing model lack of fit -- Quantifying life history traits.;

506 a| Available only to authorized users.;

520 a| " Bayesian Analysis of Capture-Recapture Data with Hidden Markov Models: Theory and Case Studies in R and NIMBLE introduces ecologists and statisticians to a powerful and unifying framework for analysing capture-recapture data. Hidden Markov models (HMMs) have become a cornerstone in modern population ecology, offering a flexible way to decompose complex processes such as survival, recruitment, and dispersal into simpler building blocks, while explicitly accounting for the fact that we only observe imperfect data rather than the true underlying states. Combined with Bayesian inference, HMMs provide a natural and transparent approach to handle uncertainty, explore model structures, and draw robust conclusions. This book illustrates how to bring these ideas to life using the R package NIMBLE, a fast-developing environment for building and fitting hierarchical models"-- Provided by publisher.;

538 a| Mode of access: World Wide Web;

650 0 a| Bayesian statistical decision theory0| http://id.loc.gov/authorities/subjects/sh85012506=| ^A20878;

650 0 a| Hidden Markov models0| http://id.loc.gov/authorities/subjects/sh2007000125=| ^A1488919;

650 0 a| R (Computer program language)0| http://id.loc.gov/authorities/subjects/sh2002004407=| ^A523882;

650 0 a| Ecologyx| Statistical methods0| http://id.loc.gov/authorities/subjects/sh2008118584=| ^A9182;

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758 4| http://id.loc.gov/ontologies/bibframe/instanceOf1| http://id.loc.gov/resources/works/in00024462726;

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Bayesian analysis of capture-recapture data with hidden Markov models theory and case studies in R / Olivier Gimenez.

Author/creator	Gimenez, Olivier, 1975
Format	Electronic
Edition	First edition.
Publication Info	Boca Raton FL : CRC Press, 2026.
Description	pages cm.
Supplemental Content	Full text available from eBooks on EBSCOhost
Subjects	Bayesian statistical decision theory Hidden Markov models R (Computer program language) Ecology--Statistical methods.

Series	CRC interdisciplinary statistics series
Contents	Bayesian statistics and MCMC -- NIMBLE tutorial -- Hidden Markov models -- Alive and dead -- Sites and states -- Dealing with covariates -- Addressing model lack of fit -- Quantifying life history traits.
Abstract	" Bayesian Analysis of Capture-Recapture Data with Hidden Markov Models: Theory and Case Studies in R and NIMBLE introduces ecologists and statisticians to a powerful and unifying framework for analysing capture-recapture data. Hidden Markov models (HMMs) have become a cornerstone in modern population ecology, offering a flexible way to decompose complex processes such as survival, recruitment, and dispersal into simpler building blocks, while explicitly accounting for the fact that we only observe imperfect data rather than the true underlying states. Combined with Bayesian inference, HMMs provide a natural and transparent approach to handle uncertainty, explore model structures, and draw robust conclusions. This book illustrates how to bring these ideas to life using the R package NIMBLE, a fast-developing environment for building and fitting hierarchical models"-- Provided by publisher.
Bibliography note	Includes bibliographical references.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2025044179
ISBN	9781032154237 hardback
ISBN	9781032154244 paperback
ISBN	ebook

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Bayesian analysis of capture-recapture data with hidden Markov models theory and case studies in R / Olivier Gimenez.

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