This second edition offers a comprehensive exploration of key concepts in statistical inference as a subset of decision theory, emphasizing information-theoretic principles. Written from a Bayesian perspective, it also reviews non-Bayesian theories and controversial topics, making it accessible to readers familiar with advanced calculus.
A comprehensive guide to simulation methods with explicit recommendations of methods and algorithms. Covers both the technical aspects of the subject, such as the generation of random numbers, non-uniform random variates and stochastic processes, and the use of simulation. Supported by the relevant mathematical theory, the text contains a great deal of unpublished research material, including coverage of the analysis of shift-register generators, sensitivity analysis of normal variate generators, analysis of simulation output, and more. Includes a selection of computer programs.