Bringing Bayesian Models to Life

Description

Cover Half Title Title Page Copyright Page Table of Contents Preface Acknowledgments Authors SECTION I: Background Chapter 1: Bayesian Models 1.1 Introduction and Statistical Notation 1.2 Probability Concepts 1.3 Modeling Concepts 1.4 Additional Concepts and Reading Chapter 2: Numerical Integration 2.1 Bayesian Integrals 2.2 Numerical Quadrature 2.3 Additional Concepts and Reading Chapter 3: Monte Carlo 3.1 Sampling 3.2 Monte Carlo Integration 3.3 Additional Concepts and Reading Chapter 4: Markov Chain Monte Carlo 4.1 Metropolis-Hastings 4.2 Metropolis-Hastings in Practice 4.3 Proposal Distributions 4.4 Gibbs Sampling 4.5 Additional Concepts and Reading Chapter 5: Importance Sampling 5.1 Additional Concepts and Reading SECTION II: Basic Models and Concepts Chapter 6: Bernoulli-Beta 6.1 MCMC with Symmetric Proposal Distributions 6.2 MCMC with Proposal Distributions Based on Transformations 6.3 Additional Concepts and Reading Chapter 7: Normal-Normal 7.1 Additional Concepts and Reading Chapter 8: Normal-Inverse Gamma 8.1 Additional Concepts and Reading Chapter 9: Normal-Normal-Inverse Gamma 9.1 Additional Concepts and Reading SECTION III: Intermediate Models and Concepts Chapter 10: Mixture Models 10.1 Additional Concepts and Reading Chapter 11: Linear Regression 11.1 Additional Concepts and Reading Chapter 12: Posterior Prediction 12.1 Additional Concepts and Reading Chapter 13: Model Comparison 13.1 Additional Concepts and Reading Chapter 14: Regularization 14.1 Additional Concepts and Reading Chapter 15: Bayesian Model Averaging 15.1 Additional Concepts and Reading Chapter 16: Time Series Models 16.1 Univariate Autoregressive Models 16.2 Autoregressive Models for Populations 16.3 Prediction with Time Series Models 16.4 Multivariate Autoregressive Models for Animal Movement 16.5 Additional Concepts and Reading Chapter 17: Spatial Models 17.1 Geostatistical Models 17.2 Bayesian Kriging 17.3 Areal Data Models 17.4 Additional Concepts and Reading SECTION IV: Advanced Models and Concepts Chapter 18: Quantile Regression 18.1 Quantile Models for Continuous Data 18.2 Additional Concepts and Reading Chapter 19: Hierarchical Models 19.1 Hierarchical Gaussian Models 19.2 Two-Stage Model Fitting Algorithms 19.3 Additional Concepts and Reading Chapter 20: Binary Regression 20.1 Generalized Linear Models 20.2 Logistic Regression 20.3 Probit Regression 20.4 Quantile Models for Binary Data 20.5 Additional Concepts and Reading Chapter 21: Count Data Regression 21.1 Poisson Regression 21.2 Resource Selection Functions and Species Distribution Models 21.3 Step Selection Functions 21.4 Poisson Time Series Models 21.5 Model Checking 21.6 Negative Binomial Regression 21.7 Quantile Models for Count Data 21.8 Binomial Models 21.9 Additional Concepts and Reading Chapter 22: Zero-Inflated Models 22.1 Mixture Models for Excess Zeros 22.2 Zero-Inflated Poisson Models 22.3 Zero-Inflated Negative Binomial Models 22.4 Additional Concepts and Reading Chapter 23: Occupancy Models 23.1 Simple Occupancy Models 23.2 General Occupancy Models 23.3 Probit Occupancy Models 23.4 Additional Concepts and Reading Chapter 24: Abundance Models 24.1 Capture-Recapture Models 24.2 Distance Sampling Models 24.3 Survival Models 24.4 N-Mixture Models 24.5 Additional Concepts and Reading SECTION V: Expert Models and Concepts Chapter 25: Integrated Population Models 25.1 Data Reconciliation 25.2 False Positive Models with Auxiliary Data 25.3 Population Vital Rate IPMs 25.4 Additional Concepts and Reading Chapter 26: Spatial Occupancy Models 26.1 Additional Concepts and Reading Chapter 27: Spatial Capture-Recapture Models 27.1 Additional Concepts and Reading Chapter 28: Spatio-temporal Models 28.1 Multivariate Time Series Models 28.2 Mechanistic Spatio-temporal Models 28.3 Additional Concepts and Reading Chapter 29: Hamiltonian Monte Carlo 29.1 Additional Concepts and Reading Tips and Tricks Glossary References Probability Distributions Index