Bringing Bayesian models to life
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 scien…
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- Full title :Bringing Bayesian models to life / Mevin B. Hooten, Trevor J. Hefley.
Summary
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.
Read More ↓Edition Statements
- Publication Date:2019
Publication Statements
- Publisher(s):CRC Press, Taylor & Francis Group
- Place of Publication:Boca Raton, FL
- Language:eng
- Publication Date:2019
Notes
"A Chapman & Hall Book"
The Physical Object
- Description:1 online resource.
Edition Identifiers
- ISBN:9780429516801, 0429516800, 9780429243653, 0429243650, 9780429513374, 0429513372, 9780429520235, 0429520239
- DOI:10.1201/9780429243653.
- oclc:on1101967000
- LC Classification:QA279.5
- Dewey Decimal:519.5/42
Table 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.
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