Applied Stochastic Modelling

University of Kent, UK University of Minnesota, Minneapolis, Minnesota, USA Northwestern University, Evanston, Illinois, USA University of British Columbia, Vancouver, Canada

• Introduction and Examples. Basic Model Fitting. Function Optimisation. Basic Likelihood Tools. General Principles. Simulation Techniques. Bayesian Methods and MCMC. General Families of Models. Index of Data Sets. Index of MATLAB Programs. Appendices. Solutions and Comments for Selected Exercises. Bibliography. Index.

Praise for the First EditionThe author’s enthusiasm for his subject shines through this book. There are plenty of interesting example data sets … The book covers much ground in quite a short space … In conclusion, I like this book and strongly recommend it. It covers many of my favourite topics. In another life, I would have liked to have written it, but Professor Morgan has made a better job if it than I would have done.—Tim Auton, Journal of the Royal Statistical Society I am seriously considering adopting Applied Stochastic Modelling for a graduate course in statistical computation that our department is offering next term.—Jim Albert, Journal of the American Statistical Association …very well written, fresh in its style, with lots of wonderful examples and problems.—R.P. Dolrow, TechnometricsA useful tool for both applied statisticians and stochastic model users of other fields, such as biologists, sociologists, geologists, and economists.—Zentralblatt MATHThe book is a delight to read, reflecting the author’s enthusiasm for the subject and his wide experience. The layout and presentation of material are excellent. Both for new research students and for experienced researchers needing to update their skills, this is an excellent text and source of reference.—Statistical Methods in Medical Research