Likelihood Methods in Biology and Ecology
A Modern Approach to Statistics
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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 a...
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.
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