Bringing Bayesian Models to Life

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A01=Mevin B. Hooten
A01=Trevor Hefley
advanced Bayesian inference in ecology
animal movement analysis
Asymmetric Laplace Distribution
Author_Mevin B. Hooten
Author_Trevor Hefley
Bayesian computational methods
Bayesian models
Category=PBWH
Category=PSV
Data Set
ecological modelling techniques
environmental science
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Full Conditional Distributions
Gibbs Updates
HMC Algorithm
Malthusian Growth Model
MCMC Algorithm
MCMC algorithms
MCMC Iteration
MCMC Loop
MCMC Output
MCMC Sample
Monte Carlo integration
Normal Data Simulation
Normal Normal Model
Occupancy Model
Posterior Distribution
Posterior Histogram
Posterior Predictive
Posterior Predictive Distribution
Probit Regression Coefficients
Proposal Distribution
Quantile Regression
Quantile Regression Model
R code implementing algorithms
R statistical programming
Small DIC
ZI Negative Binomial Model
ZI Poisson Regression Model

Product details

  • ISBN 9780367198480
  • Weight: 1020g
  • Dimensions: 156 x 234mm
  • Publication Date: 11 Jun 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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.

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.

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