Bayesian Analysis for Population Ecology
Bayesian Analysis for Population Ecology
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种群生态学的贝叶斯分析
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
S. Brooks
中科院分区：
文献类型：
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作者：
Ruth King;B. Morgan;O. Gimenez;S. Brooks
介绍生态数据的统计分析介绍人口生态保护和管理数据和模型贝叶斯和经典统计推断衰老数据，模型和可能性介绍人口数据建模生存多地点，多重状态和运动数据协变量与大数据集衰老组合信息建模生产率参数Redundancy基于Likestruction的经典推理简单似然模型选择最大化对数似然置信区域计算机包贝叶斯技术和工具贝叶斯推理介绍先验选择和启发先验灵敏度分析总结后验分布有向非循环图马尔可夫链蒙特卡罗蒙特卡罗积分马尔可夫链马尔可夫链蒙特卡罗（MCMC）实现MCMC模型判别介绍贝叶斯模型判别估计后验模型概率先验灵敏度模型平均边际后验分布评估时间/年龄依赖性改进和检查性能附加计算技术MCMC和RJMCMC计算机程序R代码（MCMC）用于北斗数据WinBUGS代码（MCMC）用于北斗数据MCMC在计算机包中MARK R代码（RJMCMC）用于模型不确定性WinBUGS代码（RJMCMC）用于模型不确定性生态应用协变量，缺失值和随机效应介绍协变量缺失值评估协变量依赖性随机效应预测样条多状态模型介绍缺失协变量/辅助变量方法模型判别和平均状态空间建模介绍Leslie基于矩阵的模型非Leslie-基于模型捕获-再捕获数据封闭总体引入模型和记法模型拟合模型判别和平均线断面附录A：常见分布离散分布连续分布附录B：R编程R入门R有用的R命令编写（RJ）MCMC函数R模型C/C代码R白色鹳协变量分析代码附录C：在WinBUGS中编程WinBUGS从R调用WinBUGS参考文献索引摘要、进一步阅读和练习出现在大多数章节的结尾。
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.