This richly illustrated textbook covers modern statistical methods with
applications in medicine, epidemiology and biology. Firstly, it
discusses the importance of statistical models in applied quantitative
research and the central role of the likelihood function, describing
likelihood-based inference from a frequentist viewpoint, and exploring
the properties of the maximum likelihood estimate, the score function,
the likelihood ratio and the Wald statistic. In the second part of the
book, likelihood is combined with prior information to perform Bayesian
inference. Topics include Bayesian updating, conjugate and reference
priors, Bayesian point and interval estimates, Bayesian asymptotics and
empirical Bayes methods. It includes a separate chapter on modern
numerical techniques for Bayesian inference, and also addresses advanced
topics, such as model choice and prediction from frequentist and
Bayesian perspectives. This revised edition of the book "Applied
Statistical Inference" has been expanded to include new material on
Markov models for time series analysis. It also features a comprehensive
appendix covering the prerequisites in probability theory, matrix
algebra, mathematical calculus, and numerical analysis, and each chapter
is complemented by exercises. The text is primarily intended for
graduate statistics and biostatistics students with an interest in
applications.