By N. Thompson Hobbs,Mevin B. Hooten
Bayesian modeling has develop into an vital instrument for ecological examine since it is uniquely fitted to care for complexity in a statistically coherent method. This textbook offers a complete and obtainable advent to the newest Bayesian methods—in language ecologists can comprehend. not like different books at the topic, this one emphasizes the foundations in the back of the computations, giving ecologists a big-picture realizing of ways to enforce this robust statistical approach.
Bayesian Models is an important primer for non-statisticians. It starts off with a definition of likelihood and develops a step by step series of hooked up principles, together with simple distribution idea, community diagrams, hierarchical types, Markov chain Monte Carlo, and inference from unmarried and a number of types. This detailed booklet locations much less emphasis on desktop coding, favoring in its place a concise presentation of the mathematical statistics had to know how and why Bayesian research works. It additionally explains how you can write out correctly formulated hierarchical Bayesian types and use them in computing, examine papers, and proposals.
This primer permits ecologists to appreciate the statistical rules in the back of Bayesian modeling and observe them to investigate, instructing, coverage, and management.
- Presents the mathematical and statistical foundations of Bayesian modeling in language available to non-statisticians
- Covers easy distribution idea, community diagrams, hierarchical versions, Markov chain Monte Carlo, and more
- Deemphasizes laptop coding in desire of easy principles
- Explains tips to write out correctly factored statistical expressions representing Bayesian models
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