Bringing Bayesian Models to Life

Bringing Bayesian Models to Life
  • eBook:
    Bringing Bayesian Models to Life
  • Author:
    Mevin B. Hooten, Trevor J. Hefley
  • Edition:
    1 edition
  • Categories:
  • Data:
    June 11, 2019
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    590 pages
  • Format:
    PDF, ePUB

Book Description
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.
  • 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.


SECTION I - Background
Chapter 1. Bayesian Models
Chapter 2. Numerical Integration
Chapter 3. Monte Carlo
Chapter 4. Markov Chain Monte Carlo
Chapter 5. Importance Sampling

SECTION II - Basic Models and Concepts
Chapter 6. Bernoulli-Beta
Chapter 7. Normal-Normal
Chapter 8. Normal-Inverse Gamma
Chapter 9. Normal-Normal-Inverse Gamma

SECTION III - Intermediate Models and Concepts
Chapter 10. Mixture Models
Chapter 11. Linear Regression
Chapter 12. Posterior Prediction
Chapter 13. Model Comparison
Chapter 14. Regularization
Chapter 15. Bayesian Model Averaging
Chapter 16. Time Series Models
Chapter 17. Spatial Models

SECTION IV - Advanced Models and Concepts
Chapter 18. Quantile Regression
Chapter 19. Hierarchical Models
Chapter 20. Binary Regression
Chapter 21. Count Data Regression
Chapter 22. Zero-Inflated Models
Chapter 23. Occupancy Models
Chapter 24. Abundance Models

SECTION V - Expert Models and Concepts
Chapter 25. Integrated Population Models
Chapter 26. Spatial Occupancy Models
Chapter 27. Spatial Capture-Recapture Models
Chapter 28. Spatio-temporal Models
Chapter 29. Hamiltonian Monte Carlo

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