000 | 05270cam a2200397 i 4500 | ||
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005 | 20211105021538.0 | ||
008 | 100426s2010 flua b 001 0 eng d | ||
010 | _a 2009026637 | ||
011 | _aBIB MATCHES WORLDCAT | ||
020 |
_a1439811873 _qhardcover (alk. paper) |
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020 |
_a9781439811870 _qhardcover (alk. paper) |
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035 | _a(ATU)b11674921 | ||
035 | _a(OCoLC)301889402 | ||
040 |
_aDLC _beng _erda _cDLC _dBTCTA _dYDXCP _dC#P _dUKM _dCDX _dATU |
||
050 | 0 | 0 |
_aQH352 _b.B38 2010 |
082 | 0 | 0 |
_a577.8801519542 _222 |
100 | 1 |
_aKing, Ruth, _d1977- _eauthor. _9448513 |
|
245 | 1 | 0 |
_aBayesian analysis for population ecology / _cRuth King [and others]. |
264 | 1 |
_aBoca Raton : _bChapman & Hall/CRC, _c[2010] |
|
264 | 4 | _c©2010 | |
300 |
_axiii, 442 pages : _billustrations ; _c25 cm. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_aunmediated _bn _2rdamedia |
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338 |
_avolume _bnc _2rdacarrier |
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490 | 1 | _aInterdisciplinary statistics series | |
504 | _aIncludes bibliographical references and index. | ||
505 | 0 | 0 |
_gPart I. _tIntroduction to Statistical Analysis of Ecological Data -- _g1. _tIntroduction -- _g1.1. _tPopulation Ecology -- _g1.2. _tConservation and Management -- _g1.3. _tData and Models -- _g1.4. _tBayesian and Classical Statistical Inference -- _g1.5. _tSenescence -- _g1.6. _tSummary -- _g1.7. _tFurther Reading -- _g1.8. _tExercises -- _g2. _tData, Models and Likelihoods -- _g2.1. _tIntroduction -- _g2.2. _tPopulation Data -- _g2.3. _tModelling Survival -- _g2.4. _tMulti-Site, Multi-State and Movement Data -- _g2.5. _tCovariates and Large Data Sets -- _g2.6. _tCombining Information -- _g2.7. _tModelling Productivity -- _g2.8. _tParameter Redundancy -- _g2.9. _tSummary -- _g2.10. _tFurther Reading -- _g2.11. _tExercises -- _g3. _tClassical Inference Based on Likelihood -- _g3.1. _tIntroduction -- _g3.2. _tSimple Likelihoods -- _g3.3. _tModel Selection -- _g3.4. _tMaximising Log-Likelihoods -- _g3.5. _tConfidence Regions -- _g3.6. _tComputer Packages -- _g3.7. _tSummary -- _g3.8. _tFurther Reading -- _g3.9. _tExercises -- _gPart II. _tBayesian Techniques and Tools -- _g4. _tBayesian Inference -- _g4.1. _tIntroduction -- _g4.2. _tPrior Selection and Elicitation -- _g4.3. _tPrior Sensitivity Analyses -- _g4.4. _tSummarising Posterior Distributions -- _g4.5. _tDirected Acyclic Graphs -- _g4.6. _tSummary -- _g4.7. _tFurther Reading -- _g4.8. _tExercises -- _g5. _tMarkov Chain Monte Carlo -- _g5.1. _tMonte Carlo Integration -- _g5.2. _tMarkov Chains -- _g5.3. _tMarkov Chain Monte Carlo -- _g5.4. _tImplementing MCMC -- _g5.5. _tSummary -- _g5.6. _tFurther Reading -- _g5.7. _tExercises -- _g6. _tModel Discrimination -- _g6.1. _tIntroduction -- _g6.2. _tBayesian Model Discrimination -- _g6.3. _tEstimating Posterior Model Probabilities -- _g6.4. _tPrior Sensitivity -- _g6.5. _tModel Averaging -- _g6.6. _tMarginalPosterior Distributions -- _g6.7. _tAssessing Temporal /Age Dependence -- _g6.8. _tImproving and Checking Performance -- _g6.9. _tAdditional Computational Techniques -- _g6.10. _tSummary -- _g6.11. _tFurther Reading -- _g6.12. _tExercises -- _g7. _tMCMC and RJMCMC Computer Programs -- _g7.1. _tR Code (MCMC) for Dipper Data -- _g7.2. _tWinBUGS Code (MCMC) for Dipper Data -- _g7.3. _tMCMC within the Computer Package MARK -- _g7.4. _tR code (RJMCMC) for Model Uncertainty -- _g7.5. _tWinBUGS Code (RJMCMC) for Model Uncertainty -- _g7.6. _tSummary -- _g7.7. _tFurther Reading -- _g7.8. _tExercises -- _gPart III. _tEcological Applications -- _g8. _tCovariates, Missing Values and Random Effects -- _g8.1. _tIntroduction -- _g8.2. _tCovariates -- _g8.3. _tMissing Values -- _g8.4. _tAssessing Covariate Dependence -- _g8.5. _tRandom Effects -- _g8.6. _tPrediction -- _g8.7. _tSplines -- _g8.8. _tSummary -- _g8.9. _tFurther Reading -- _g9. _tMulti-State Models -- _g9.1. _tIntroduction -- _g9.2. _tMissing Covariate /Auxiliary Variable Approach -- _g9.3. _tModel Discrimination and Averaging -- _g9.4. _tSummary -- _g9.5. _tFurther Reading -- _g10. _tState-Space Modelling -- _g10.1. _tIntroduction -- _g10.2. _tLeslie Matrix-Based Models -- _g10.3. _tNon-Leslie-Based Models -- _g10.4. _tCapture-Recapture Data -- _g10.5. _tSummary -- _g10.6. _tFurther Reading -- _g11. _tClosed Populations -- _g11.1. _tIntroduction -- _g11.2. _tModels and Notation -- _g11.3. _tModel Fitting -- _g11.4. _tModel Discrimination and Averaging -- _g11.5. _tLine Transects -- _g11.6. _tSummary -- _g11.7. _tFurther Reading -- _gAppendix A. _tCommon Distributions -- _gAppendix A.1. _tDiscrete Distributions -- _gAppendix A.2. _tContinuous Distributions -- _gAppendix B. _tProgramming in R -- _gAppendix B.1. _tGetting Started in R -- _gAppendix B.2. _tUseful R Commands -- _gAppendix B.3. _tWriting (RJ)MCMC Functions -- _gAppendix B.4. _tR Code for Model C /C -- _gAppendix B.5. _tR Code for White Stork Covariate Analysis -- _gAppendix B.6. _tSummary -- _gAppendix C. _tProgramming in WinBUGS -- _gAppendix C.1. _tWinBUGS -- _gAppendix C.2. _tCalling WinBUGS from R -- _gAppendix C.3. _tSummary. |
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