東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Bayesian population analysis using WinBUGS = a hierarchical perspective /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian population analysis using WinBUGS/ Marc Kery and Michael Schaub ; foreword by Steven R. Beissinger.
Reminder of title:	a hierarchical perspective /
Author:	K�ery, Marc.{me_controlnum}
other author:	Schaub, Michael.
Published:	Waltham, MA :Academic Press, : 2012.,
Description:	1 online resource (xvii, 535 p.) :ill. (some col.)
[NT 15003449]:	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.
Subject:	Population biology - Data processing. -
Online resource:	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 /
LDR:03454cmm 2200445Ka 4500 001 1325630
005 20120813074940.0
006 m d
007 cr cn|||||||||
008 130122s2012 maua ob 001 0 eng d
019 $a 759686465
020 $a 9780123870209 (electronic bk.)
020 $a 0123870208 (electronic bk.)
020 $a 9780123870216 (electronic bk.)
020 $a 0123870216 (electronic bk.)
029 1 $a DEBBG $b BV039829653
029 1 $a AU@ $b 000048718242
035 $a ocn760173084
037 $a 1106587:10995121 $b Elsevier Science & Technology $n http://www.sciencedirect.com
040 $a OPELS $b eng $c OPELS $d CDX $d NTE $d OCLCQ $d YDXCP $d EBLCP $d E7B $d MHW $d REDDC $d OCLCQ $d N$T
049 $a NTYA
050 4 $a QH352 $b .K47 2012
072 7 $a NAT $x 010000 $2 bisacsh
072 7 $a NAT $x 045040 $2 bisacsh
072 7 $a SCI $x 026000 $2 bisacsh
072 7 $a SCI $x 020000 $2 bisacsh
082 0 4 $a 577.8/80285 $2 23
100 1 $a K�ery, Marc.{me_controlnum} $3 1613463
245 1 0 $a Bayesian population analysis using WinBUGS $h [electronic resource] : $b a hierarchical perspective / $c Marc Kery and Michael Schaub ; foreword by Steven R. Beissinger.
250 $a 1st ed.
260 $a Waltham, MA : $b Academic Press, $c 2012.
300 $a 1 online resource (xvii, 535 p.) : $b ill. (some col.)
504 $a Includes bibliographical references and index.
505 0 $a 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.
520 $a 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.
588 $a Description based on print version record.
630 0 0 $a WinBUGS. $3 1535346
650 0 $a Population biology $x Data processing. $3 1613465
650 4 $a Biology. $3 522710
650 4 $a WinBUGS. $3 1613466
650 4 $a Science. $3 516376
650 4 $a Natural history. $3 526335
650 7 $a NATURE / Ecology $2 bisacsh $3 1613467
650 7 $a NATURE / Ecosystems & Habitats / Wilderness $2 bisacsh $3 1613468
650 7 $a SCIENCE / Environmental Science $2 bisacsh $3 1599523
650 7 $a SCIENCE / Life Sciences / Ecology $2 bisacsh $3 1613469
655 4 $a Electronic books. $2 lcsh $3 542853
700 1 $a Schaub, Michael. $3 1613464
776 0 8 $i Print version: $a K�ery, Marc. $t Bayesian population analysis using WinBUGS. $b 1st ed. $d Waltham, MA : Academic Press, 2012 $z 9780123870209 $w (DLC) 2011029641 $w (OCoLC)731925446
856 4 0 $3 ScienceDirect $u http://www.sciencedirect.com/science/book/9780123870209
938 $a Coutts Information Services $b COUT $n 19064937
938 $a YBP Library Services $b YANK $n 7166866
938 $a EBL - Ebook Library $b EBLB $n EBL767268
938 $a ebrary $b EBRY $n ebr10503269