This is an entry-level book on Bayesian statistics written in a casual, and conversational tone. The authors walk a reader through many sample problems step-by-step to provide those with little background in math or statistics with the vocabulary, notation, and understanding of the calculations used in many Bayesian problems.
This is an entry-level book on Bayesian statistics written in a casual, and conversational tone. The authors walk a reader through many sample problems step-by-step to provide those with little background in math or statistics with the vocabulary, notation, and understanding of the calculations used in many Bayesian problems.
Therese Donovan is a wildlife biologist with the U.S. Geological Survey, Vermont Cooperative Fish and Wildlife Research Unit. Based in the Rubenstein School of Environment and Natural Resources at the University of Vermont, Therese teaches graduate courses on ecological modeling and conservation biology. She works with a variety of student and professional collaborators on research problems focused on the conservation of vertebrates. Therese is the Director of the Vermont Cooperative Fish and Wildlife Unit Spreadsheet Project, a suite of on-line tutorials in Excel and R for modeling and analysis of wildlife populations. She lives in Vermont with her husband, Peter, and two children, Evan and Ana. Ruth Mickey is a Professor Emerita of Statistics at the University of Vermont. Most of Ruth's career was spent in the Department of Mathematics and Statistics, where she taught courses in Applied Multivariate Analysis, Categorical Data, Survey Sampling, Analysis of Variance and Regression, and Probability. She served as an advisor or committee member of numerous MS and PhD committees over a broad range of academic disciplines. She worked on the development of statistical methods and applications to advance public health and natural resources issues throughout her career.
Inhaltsangabe
* Section 1 * Basics of Probability * 1: Introduction to Probability * 2: Joint, Marginal, and Conditional Probability * Section 2 * Bayes' Theorem and Bayesian Inference * 3: Bayes' Theorem * 4: Bayesian Inference * 5: The Author Problem - Bayesian Inference with Two Hypotheses * 6: The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses * 7: The Portrait Problem: Bayesian Inference with Joint Likelihood * Section 3 * Probability Functions * 8: Probability Mass Functions * 9: Probability Density Functions * Section 4 * Bayesian Conjugates * 10: The White House Problem: The Beta-Binomial Conjugate * 11: The Shark Attack Problem: The Gamma-Poisson Conjugate * 12: The Maple Syrup Problem: The Normal-Normal Conjugate * Section 5 * Markov Chain Monte Carlo * 13: The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm * 14: MCMC Diagnostic Approaches * 15: The White House Problem Revisited: MCMC with the Metropolis-Hastings Algorithm * 16: The Maple Syrup Problem Revisited: MCMC with Gibbs Sampling * Section 6 * Applications * 17: The Survivor Problem: Simple Linear Regression with MCMC * 18: The Survivor Problem Continued: Introduction to Bayesian Model Selection * 19: The Lorax Problem: Introduction to Bayesian Networks * 20: The Once-ler Problem: Introduction to Decision Trees * Appendices * Appendix 1: The Beta-Binomial Conjugate Solution * Appendix 2: The Gamma-Poisson Conjugate Solution * Appendix 3: The Normal-Normal Conjugate Solution * Appendix 4: Conjugate Solutions for Simple Linear Regression * Appendix 5: The Standardization of Regression Data
* Section 1 * Basics of Probability * 1: Introduction to Probability * 2: Joint, Marginal, and Conditional Probability * Section 2 * Bayes' Theorem and Bayesian Inference * 3: Bayes' Theorem * 4: Bayesian Inference * 5: The Author Problem - Bayesian Inference with Two Hypotheses * 6: The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses * 7: The Portrait Problem: Bayesian Inference with Joint Likelihood * Section 3 * Probability Functions * 8: Probability Mass Functions * 9: Probability Density Functions * Section 4 * Bayesian Conjugates * 10: The White House Problem: The Beta-Binomial Conjugate * 11: The Shark Attack Problem: The Gamma-Poisson Conjugate * 12: The Maple Syrup Problem: The Normal-Normal Conjugate * Section 5 * Markov Chain Monte Carlo * 13: The Shark Attack Problem Revisited: MCMC with the Metropolis Algorithm * 14: MCMC Diagnostic Approaches * 15: The White House Problem Revisited: MCMC with the Metropolis-Hastings Algorithm * 16: The Maple Syrup Problem Revisited: MCMC with Gibbs Sampling * Section 6 * Applications * 17: The Survivor Problem: Simple Linear Regression with MCMC * 18: The Survivor Problem Continued: Introduction to Bayesian Model Selection * 19: The Lorax Problem: Introduction to Bayesian Networks * 20: The Once-ler Problem: Introduction to Decision Trees * Appendices * Appendix 1: The Beta-Binomial Conjugate Solution * Appendix 2: The Gamma-Poisson Conjugate Solution * Appendix 3: The Normal-Normal Conjugate Solution * Appendix 4: Conjugate Solutions for Simple Linear Regression * Appendix 5: The Standardization of Regression Data
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