Muutke küpsiste eelistusi

E-raamat: Bayesian Population Analysis using WinBUGS: A Hierarchical Perspective

4.57/5 (27 hinnangut Goodreads-ist)
(Head of Ecology, Swiss Ornithological Institute, Sempach, Switzerland), (Senior Scientist, Swiss Ornithological Institute, Basel, Switzerland)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 11-Oct-2011
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780123870216
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 55,56 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Bayesian Population Analysis using WinBUGS: A Hierarchical PerspectiveSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 11-Oct-2011
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780123870216
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

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.

Muu info

A comprehensive collection of richly commented Bayesian analyses of ecological models for population analysis using WinBUGS run from within program R, bridging the gap between introductory- and advance-level texts
Foreword xi
Preface xiii
Acknowledgments xvii
1 Introduction
1.1 Ecology: The Study of Distribution and Abundance and of the Mechanisms Driving Their Change
1(5)
1.2 Genesis of Ecological Observations
6(3)
1.3 The Binomial Distribution as a Canonical Description of the Observation Process
9(4)
1.4 Structure and Overview of the Contents of this Book
13(3)
1.5 Benefits of Analyzing Simulated Data Sets: An Example of Bias and Precision
16(4)
1.6 Summary and Outlook
20(1)
1.7 Exercises
21(2)
2 Brief Introduction to Bayesian Statistical Modeling
2.1 Introduction
23(1)
2.2 Role of Models in Science
24(3)
2.3 Statistical Models
27(1)
2.4 Frequentist and Bayesian Analysis of Statistical Models
28(10)
2.5 Bayesian Computation
38(1)
2.6 WinBUGS
38(3)
2.7 Advantages and Disadvantages of Bayesian Analyses by Posterior Sampling
41(2)
2.8 Hierarchical Models
43(1)
2.9 Summary and Outlook
44(4)
3 Introduction to the Generalized Linear Model: The Simplest Model for Count Data
3.1 Introduction
48(1)
3.2 Statistical Models: Response = Signal + Noise
48(7)
3.3 Poisson GLM in R and WinBUGS for Modeling Time Series of Counts
55(11)
3.4 Poisson GLM for Modeling Fecundity
66(1)
3.5 Binomial GLM for Modeling Bounded Counts or Proportions
67(4)
3.6 Summary and Outlook
71(1)
3.7 Exercises
72(1)
4 Introduction to Random Effects: Conventional Poisson GLMM for Count Data
4.1 Introduction
73(9)
4.2 Accounting for Overdispersion by Random Effects-Modeling in R and WinBUGS
82(8)
4.3 Mixed Models with Random Effects for Variability among Groups (Site and Year Effects)
90(20)
4.4 Summary and Outlook
110(2)
4.5 Exercises
112(3)
5 State-Space Models for Population Counts
5.1 Introduction
115(3)
5.2 A Simple Model
118(3)
5.3 Systematic Bias in the Observation Process
121(5)
5.4 Real Example: House Martin Population Counts in the Village of Magden
126(5)
5.5 Summary and Outlook
131(1)
5.6 Exercises
131(3)
6 Estimation of the Size of a Closed Population from Capture-Recapture Data
6.1 Introduction
134(5)
6.2 Generation and Analysis of Simulated Data with Data Augmentation
139(18)
6.3 Analysis of a Real Data Set: Model Mtbh for Species Richness Estimation
157(5)
6.4 Capture-Recapture Models with Individual Covariates: Model Mt+x
162(7)
6.5 Summary and Outlook
169(1)
6.6 Exercises
170(2)
7 Estimation of Survival from Capture-Recapture Data Using the Cormack-Jolly-Seber Model
7.1 Introduction
172(3)
7.2 The CJS Model as a State-Space Model
175(2)
7.3 Models with Constant Parameters
177(6)
7.4 Models with Time-Variation
183(9)
7.5 Models with Individual Variation
192(7)
7.6 Models with Time and Group Effects
199(9)
7.7 Models with Age Effects
208(4)
7.8 Immediate Trap Response in Recapture Probability
212(4)
7.9 Parameter Identifiability
216(4)
7.10 Fitting the CJS to Data in the M-Array Format: The Multinomial Likelihood
220(11)
7.11 Analysis of a Real Data Set: Survival of Female Leisler's Bats
231(6)
7.12 Summary and Outlook
237(1)
7.13 Exercises
238(3)
8 Estimation of Survival Using Mark-Recovery Data
8.1 Introduction
241(2)
8.2 The Mark-Recovery Model as a State-Space Model
243(5)
8.3 The Mark-Recovery Model Fitted with the Multinomial Likelihood
248(7)
8.4 Real-Data Example: Age-Dependent Survival in Swiss Red Kites
255(6)
8.5 Summary and Outlook
261(1)
8.6 Exercises
261(3)
9 Estimation of Survival and Movement from Capture-Recapture Data Using Multistate Models
9.1 Introduction
264(4)
9.2 Estimation of Movement between Two Sites
268(13)
9.3 Accounting for Temporary Emigration
281(7)
9.4 Estimation of Age-Specific Probability of First Breeding
288(7)
9.5 Joint Analysis of Capture-Recapture and Mark-Recovery Data
295(5)
9.6 Estimation of Movement among Three Sites
300(7)
9.7 Real-Data Example: The Showy Lady's Slipper
307(4)
9.8 Summary and Outlook
311(1)
9.9 Exercises
312(4)
10 Estimation of Survival, Recruitment, and Population Size from Capture-Recapture Data Using the Jolly-Seber Model
10.1 Introduction
316(1)
10.2 The JS Model as a State-Space Model
317(2)
10.3 Fitting the JS Model with Data Augmentation
319(9)
10.4 Models with Constant Survival and Time-Dependent Entry
328(7)
10.5 Models with Individual Capture Heterogeneity
335(4)
10.6 Connections between Parameters, Further Quantities and Some Remarks on Identifiability
339(2)
10.7 Analysis of a Real Data Set: Survival, Recruitment and Population Size of Leisler's Bats
341(4)
10.8 Summary and Outlook
345(1)
10.9 Exercises
346(2)
11 Estimation of Demographic Rates, Population Size, and Projection Matrices from Multiple Data Types Using Integrated Population Models
11.1 Introduction
348(2)
11.2 Developing an Integrated Population Model (IPM)
350(7)
11.3 Example of a Simple IPM (Counts, Capture-Recapture, Reproduction)
357(6)
11.4 Another Example of an IPM: Estimating Productivity without Explicit Productivity Data
363(3)
11.5 IPMs for Population Viability Analysis
366(5)
11.6 Real Data Example: Hoopoe Population Dynamics
371(8)
11.7 Summary and Outlook
379(1)
11.8 Exercises
380(3)
12 Estimation of Abundance from Counts in Metapopulation Designs Using the Binomial Mixture Model
12.1 Introduction
383(5)
12.2 Generation and Analysis of Simulated Data
388(8)
12.3 Analysis of Real Data: Open-Population Binomial Mixture Models
396(13)
12.4 Summary and Outlook
409(2)
12.5 Exercises
411(3)
13 Estimation of Occupancy and Species Distributions from Detection/Nondetection Data in Metapopulation Designs Using Site-Occupancy Models
13.1 Introduction
414(5)
13.2 What Happens When p < 1 and Constant and p is Not Accounted for in a Species Distribution Model?
419(1)
13.3 Generation and Analysis of Simulated Data for Single-Season Occupancy
420(7)
13.4 Analysis of Real Data Set: Single-Season Occupancy Model
427(9)
13.5 Dynamic (Multiseason) Site-Occupancy Models
436(14)
13.6 Multistate Occupancy Models
450(9)
13.7 Summary and Outlook
459(1)
13.8 Exercises
460(4)
14 Concluding Remarks
14.1 The Power and Beauty of Hierarchical Models
464(8)
14.2 The Importance of the Observation Process
472(2)
14.3 Where Will We Go?
474(2)
14.4 The Importance of Population Analysis for Conservation and Management
476(3)
Appendix 1 A List of WinBUGS Tricks 479(8)
Appendix 2 Two Further Useful Multistate Capture-Recapture Models 487(10)
References 497(18)
Index 515
Dr. Marc works as a senior scientist at the Swiss Ornithological Institute, Seerose 1, 6204 Sempach, Switzerland. This is a non-profit NGO with about 160 employees dedicated primarily to bird research, monitoring, and conservation. Marc was trained as a plant population ecologist at the Swiss Universities of Basel and Zuerich. After a 2-year postdoc at the (then) USGS Patuxent Wildlife Center in Laurel, MD. During the last 20 years he has worked at the interface between population ecology, biodiversity monitoring, wildlife management, and statistics. He has published more than 100 peer-reviewed journal articles and five textbooks on applied statistical modeling. He has also been very active in teaching fellow biologists and wildlife managers the concepts and tools of modern statistical analysis in their fields in workshops all over the world, something which goes together with his books, which target the same audiences. Michael Schaub is the Head of the Ecology Department at the Swiss Ornithological Institute and a courtesy Professor at the University of Bern. His research interests include population dynamics, capture-recapture models, integrated population models, and migratory birds. He has coauthored approximately 130 peer-reviewed journal publications and the book Bayesian Population Analysis using WinBUGS.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97801238702162e.html
Märksõnad: