Statistical Modeling and Computation

Dirk P. Kroese is a Professor of Mathematics and Statistics at The University of Queensland. He is fascinated by anything that deals with the theory and application of randomness. He has written over 90 publications in a wide range of areas in probability and statistics,  including three influential books: The Cross-Entropy Method and Simulation and the Monte Carlo Method, Second Edition, both with Reuven Rubinstein, and Handbook of Monte Carlo Methods, with Thomas Taimre and Zdravko Botev. Joshua Chan is a Senior Lecturer at the Research School of Economics, Australian National University. His current research focuses on detecting and modeling time-varying structures in macroeconomic data using simulation-based methods. He has published widely in leading international journals such as Journal of Econometrics, Journal of Business and Economic Statistics, and Journal of Computational and Graphical Statistics.

"Fundamentals of probability and modeling are presented in a rigorous language and the transition to more advanced chapters is almost smooth. Explanations are precise, both verbally and mathematically. Throughout the book, cross-references are made so that the reader can find further or related topics in other parts of the book. Readers interested in mathematical rigor will find this book rewarding. ... Another strength of the book lies in the wealth and variety of exercises at the end of each chapter. The exercises (some with complete solutions) range from mathematical proofs and model building to programming. Solutions for select problems are presented at the end of the book. ... [This] book is outstanding in terms of coverage of topics, rigorous language and integration of computation." (Abdolvahab Khademi, Journal of Statistical Software, August 2015)

• ​​​​Probability Models.- Random Variables and Probability Distributions.- Joint Distributions.- Common Statistical Models.- Statistical Inference.- Likelihood.- Monte Carlo Sampling.- Bayesian Inference.- Generalized Linear Models.- Dependent Data Models.- State Space Models.- References.- Solutions.- MATLAB Primer.- Mathematical Supplement.- Index.