Bayesian methods for ecology
β Scribed by Michael A McCarthy
- Publisher
- Cambridge University Press
- Year
- 2007
- Tongue
- English
- Leaves
- 312
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
The interest in using Bayesian methods in ecology is increasing, however many ecologists have difficulty with conducting the required analyses. McCarthy bridges that gap, using a clear and accessible style. The text also incorporates case studies to demonstrate mark-recapture analysis, development of population models and the use of subjective judgement. The advantages of Bayesian methods, are also described here, for example, the incorporation of any relevant prior information and the ability to assess the evidence in favour of competing hypotheses. Free software is available as well as an accompanying web-site containing the data files and WinBUGS codes. Bayesian Methods for Ecology will appeal to academic researchers, upper undergraduate and graduate students of Ecology
β¦ Table of Contents
Content: Introduction --
Critiques of statistical methods --
Analysing averages and frequencies --
How good are the models? --
Regression and correlation --
Analysis of variance --
Case studies --
Mark-recapture analysis --
Effects of marking frogs --
Population dynamics --
Subjective priors --
Conclusion --
Appendices: A. A tutorial for running WinBUGS ; B. Probability distributions ; C. MCMC algorithms.
π SIMILAR VOLUMES
The interest in using Bayesian methods in ecology is increasing, however many ecologists have difficulty with conducting the required analyses. McCarthy bridges that gap, using a clear and accessible style. The text also incorporates case studies to demonstrate mark-recapture analysis, development
Master Bayesian Inference through Practical Examples and ComputationβWithout Advanced Mathematical Analysis Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial exam
<p>Broadening its scope to nonstatisticians, <strong>Bayesian Methods for Data Analysis, Third Edition</strong> provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierar
Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical m