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Bayesian Analysis for Population Ecology

Bayesian Analysis for Population Ecology by Ruth King
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INTRODUCTION TO STATISTICAL ANALYSIS OF ECOLOGICAL DATA

Introduction

Population Ecology

Conservation and Management

Data and Models

Bayesian and Classical Statistical Inference

Senescence

Data, Models and Likelihoods

Introduction

Population Data

Modelling Survival

Multi-Site, Multi-State and Movement Data

Covariates and Large Data Sets; Senescence

Combining Information

Modelling Productivity

Parameter Redundancy

Classical Inference Based on the Likelihood

Introduction

Simple Likelihoods

Model Selection

Maximising Log-Likelihoods

Confidence Regions

Computer Packages

BAYESIAN TECHNIQUES AND TOOLS

Bayesian Inference

Introduction

Prior Selection and Elicitation

Prior Sensitivity Analyses

Summarising Posterior Distributions

Directed Acyclic Graphs

Markov Chain Monte Carlo

Monte Carlo Integration

Markov Chains

Markov Chain Monte Carlo (MCMC)

Implementing MCMC

Model Discrimination

Introduction

Bayesian Model Discrimination

Estimating Posterior Model Probabilities

Prior Sensitivity

Model Averaging

Marginal Posterior Distributions

Assessing Temporal/Age Dependence

Improving and Checking Performance

Additional Computational Techniques

MCMC and RJMCMC Computer Programs

R Code (MCMC) for Dipper Data

WinBUGS Code (MCMC) for Dipper Data

MCMC within the Computer Package MARK

R code (RJMCMC) for Model Uncertainty

WinBUGS Code (RJMCMC) for Model Uncertainty

ECOLOGICAL APPLICATIONS

Covariates, Missing Values and Random Effects

Introduction

Covariates

Missing Values

Assessing Covariate Dependence

Random Effects

Prediction

Splines

Multi-State Models

Introduction

Missing Covariate/Auxiliary Variable Approach

Model Discrimination and Averaging

State-Space Modelling

Introduction

Leslie Matrix-Based Models

Non-Leslie-Based Models

Capture-Recapture Data

Closed Populations

Introduction

Models and Notation

Model Fitting

Model Discrimination and Averaging

Line Transects

Appendix A: Common Distributions

Discrete Distributions

Continuous Distributions

Appendix B: Programming in R

Getting Started in R

Useful R Commands

Writing (RJ)MCMC Functions

R Code for Model C/C

R Code for White Stork Covariate Analysis

Appendix C: Programming in WinBUGS

WinBUGS

Calling WinBUGS from R

References

Index

A Summary, Further Reading, and Exercises appear at the end of most chapters.

CRC Press; October 2009
457 pages; ISBN 9781439811887
Read online, or download in secure PDF format
Title: Bayesian Analysis for Population Ecology
Author: Ruth King; Byron Morgan; Olivier Gimenez; Steve Brooks
 
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