Description
Likelihood Methods in Biology and Ecology
A Modern Approach to Statistics
Author: Brimacombe Michael
Language: EnglishSubjects for Likelihood Methods in Biology and Ecology:
Keywords
Roc Curve; Higher Order Taylor Expansions; ANOVA Table; Random Effects Logistic Model; Lasso Approach; Observed Likelihood Function; Linear Regression Structure; Bayes Factor Values; Pivotal Quantities; Prior Densities; Bayes Factor; Non-informative Prior; ANOVA Model; Jeffreys Prior; Posterior Density; Bayesian Predictive Distribution; Independent Normal Priors; Likelihood Function; Sea Lice; Small SSE; Average Body Mass; Posterior Odds Ratios; Rainbow Trout Population; Generalized Linear Model Setting; Kullback Liebler Distance
Publication date: 12-2020
· 15.6x23.4 cm · Paperback
Publication date: 01-2019
306 p. · 15.6x23.4 cm · Hardback
Description
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Likelihood Methods in Biology and Ecology: A Modern Approach to Statistics emphasizes the importance of the likelihood function in statistical theory and applications and discusses it in the context of biology and ecology. Bayesian and frequentist methods both use the likelihood function and provide differing but related insights. This is examined here both through review of basic methodology and also the integrated use of these approaches in case studies.
Features:
- Discusses the likelihood function in both Bayesian and frequentist contexts.
- Reviews and discusses standard methods of data analysis, model selection and statistical analysis, and how to apply and interpret them in real world situations.
- Examines the application of statistical methods to observed data in the context of case studies drawn from biology and ecology.
- Uniquely discusses frequentist and Bayesian approaches to statistics as complementary allowing many standard approaches to be presented in a single book.
- Poses questions to ask when planning the design and analysis of a study or experiment.
This book is written for applied researchers, scientists, consultants, statisticians and applied scientists. Although it uses examples drawn from biology, the methods here can be applied to a wide variety of research areas and provides an accessible handbook of available statistical methods for scientific settings where there is an assumed theoretical model that can be represented using a likelihood function.
Michael Brimacombe