Bayesian Inference
with ecological applications. Reference
(Sprache: Englisch)
Statistical theory is primarily a product of the twentieth century. The prevailing school of thought builds on the frequentist philosophy developed by R.A. Fisher, the eminent biological theorist and experimentalist. Fisher's philosophy has been so...
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Statistical theory is primarily a product of the twentieth century. The prevailing school of thought builds on the frequentist philosophy developed by R.A. Fisher, the eminent biological theorist and experimentalist. Fisher's philosophy has been so thoroughly embraced that it has been labeled the "classical" approach, even though the alternative Bayesian philosophy antedates it by more than a century. Frequentist thinking has prevailed over Bayesian primarily because of the practical difficulty of fitting all but the simplest Bayesian models. Wildlife statistics has been almost entirely conducted in the frequentist mode. However, wildlife data are most naturally described in terms of hierarchical models, and these models are best analyzed using Bayesian tools. The advent of fast personal computers and easily available software has nearly removed the difficulties in fitting Bayesian models, and hierarchical models in particular. Hierarchical models describe stochastic population processes governing data and these processes are the real focus of scientific inquiry. This book takes the reader into the domain of Bayesian inference where complex hierarchical modelling is made possible.
Klappentext zu „Bayesian Inference “
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.
Inhaltsverzeichnis zu „Bayesian Inference “
Chapter 1. Bayesian InferenceChapter 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
Bibliographische Angaben
- Autoren: William A. Link , Richard J. Barker
- 2009, 330 Seiten, Maße: 19,3 x 23,6 cm, Gebunden, Englisch
- Verlag: Academic Press
- ISBN-10: 0123748542
- ISBN-13: 9780123748546
Sprache:
Englisch
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