Bayesian Models: A Statistical Primer for Ecologists

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Bayesian modeling is becoming an essential tool for ecological study since it is uniquely suitable for cope with complexity in a statistically coherent method.

• Presents the mathematical and statistical foundations of Bayesian modeling in vocabulary accessible to non-statisticians
• Covers simple distribution theory, network diagrams, hierarchical versions, Markov chain Monte Carlo, and even more
• Deemphasizes computer coding and only basics
• Explains how exactly to write out correctly factored statistical expressions representing Bayesian versions

Bayesian Versions is an important primer for non-statisticians. It starts with a description of probability and evolves a step-by-stage sequence of connected tips, including simple distribution theory, network diagrams, hierarchical versions, Markov chain Monte Carlo, and inference from solitary and multiple models. In addition, it explains how to create correctly formulated hierarchical Bayesian versions and utilize them in computing, study papers, and proposals. This original book places less focus on pc coding, favoring rather a concise display of the mathematical stats had a need to understand how and just why Bayesian analysis functions.

This primer allows ecologists to comprehend the statistical concepts behind Bayesian modeling and apply them to analyze, teaching, policy, and administration. This textbook offers a comprehensive and available introduction to the most recent Bayesian methods―in vocabulary ecologists can understand.

See also

• Chronobiology: Biological Timekeeping
• The Ecology of Plants, Second Edition
• Exercises for the Botany Laboratory