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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.
List of contents
Background. Bayesian Models. Numerical Integration. Monte Carlo. Markov Chain Monte Carlo. Importance Sampling. Basic Models and Concepts. Bernoulli - Beta. Normal-Normal. Normal-Inverse Gamma. Normal-Normal-Inverse Gamma. Intermediate Models and Concepts. Mixture Models. Linear Regression. Posterior Prediction. Model Comparison. Regularization. Bayesian Model Averaging. Time Series Models. Spatial Models. Advanced Models and Concepts. Quantile Regression. Hierarchical Models.Binary Regression. Count Data Regression. Zero-Inflated Models. Occupancy Models. Abundance Models. Expert Models and Concepts. Integrated Population Models. Spatial Occupancy Models. Spatial Capture-Recapture Models. Spatio-Temporal Models. Hamiltonian Monte Carlo.
About the author
Mevin B. Hooten is a Professor in the Departments of Fish, Wildlife, & Conservation Biology and Statistics at Colorado State University. He is also Assistant Unit Leader of the Colorado Cooperative Fish and Wildlife Research Unit (U.S. Geological Survey) and a Fellow of the American Statistical Association. He earned his PhD in Statistics at the University of Missouri and focuses on the development of statistical methodology for spatial and spatio-temporal ecological processes.
Trevor J. Hefley is an Assistant Professor in the Department of Statistics at Kansas State University. He earned a PhD in Statistics and Natural Resource Science at the University of Nebraska - Lincoln and focuses on developing and applying spatiotemporal statistical methods to inform conservation and management of fish and wildlife populations.
Summary
This book presents details and algorithms for fully Bayesian computation, primarily for students and practitioners in the ecological and environmental sciences. It empowers the reader to extend and enhance statistical models for such data without software limitations.
