Incluye bibliografía: páginas 547-566 e índice: páginas 569-573

Glosario: páginas 543-546

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.

Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement statistical models for ecological and environmental data analysis. We open the black box and show the reader how to connect modern statistical models to computer algorithms. These algorithms allow the user to fit models that answer their scientific questions without needing to rely on automated Bayesian software. We show how to handcraft statistical models that are useful in ecological and environmental science including: linear and generalized linear models, spatial and time series models, occupancy and capture-recapture models, animal movement models, spatio-temporal models, and integrated population-models. Features: R code implementing algorithms to fit Bayesian models using real and simulated data examples. A comprehensive review of statistical models commonly used in ecological and environmental science. Overview of Bayesian computational methods such as importance sampling, MCMC, and HMC. Derivations of the necessary components to construct statistical algorithms from scratch. Bringing Bayesian Models to Life contains a comprehensive treatment of models and associated algorithms for fitting the models to data. We provide detailed and annotated R code in each chapter and apply it to fit each model we present to either real or simulated data for instructional purposes. Our code shows how to create every result and figure in the book so that readers can use and modify it for their own analyses. We provide all code and data in an organized set of directories available at the authors' websites. Inglés