Bayesian Inference, 1st Edition

with ecological applications

 
Bayesian Inference, 1st Edition,William Link,Richard Barker,ISBN9780123748546
 
 
 

  &      

Academic Press

9780123748546

9780080889801

400

240 X 197

A mathematically sound but accessible and engaging introduction to the Bayesian paradigm, written specifically for ecologists and wildlife biologists.

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Key Features

• Engagingly written text specifically designed to demystify a complex subject
• Examples drawn from ecology and wildlife research
• An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference
• Companion website with analytical software and examples
• Leading authors with world-class reputations in ecology and biostatistics

Description

This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context.

The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists.

Readership

Students and researchers in animal ecology, population ecology, wildlife management, conservation biology, and ecological and biological statistics.

William Link

Richard Barker

Bayesian Inference, 1st Edition

Chapter 1. Bayesian Inference
Chapter 2. Probability
Chapter 3. Statistical Inference
Chapter 4. Posterior Calculations
Chapter 5. Bayesian Prediction
Chapter 6. Priors
Chapter 7. Multimodel Inference
Chapter 8. Hidden Data Models
Chapter 9. Closed-Population Mark-Recapture Models
Chapter 10. Latent Multinomials
Chapter 11. Open Population Models
Chapter 12. Individual Fitness
Chapter 13. Autoregressive Smoothing
 
 
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