Statistical and Inductive Inference by Minimum Message Length

Reviews / Votes

From the reviews:

"The subject matter is highly technical, and the book is correspondingly detailed. The book is intended for graduate-level courses, and should be effective in that role if the instructor is sufficiently expert in the area. For researchers at the postdoctoral level, the book will provide a wealth of information about the field.. [T]he book is likely to remain the primary reference in the field for many years to come." (Donald RICHARDS, JASA, June 2009, Vol. 104, No. 486)

"Any statistician interested in the foundations of the discipline, or the deeper philosophical issues of inference, will find this volume a rewarding read." (International Statistical Institute, December 2005)

"This very significant monograph covers the topic of the Minimum Message Length (MML) principle, a new approach to induction, hypothesis testing, model selection, and statistical inference. . This valuable book covers the topics at a level suitable for professionals and graduate students in Statistics, Computer Science, Data Mining, Machine Learning, Estimation and Model-selection, Econometrics etc." (Jerzy Martyna, Zentralblatt MATH, Vol. 1085, 2006)

"This book is around a simple idea: 'The best explanation of the facts is the shortest'. . The book applies the above idea to statistical estimation in a Bayesian context. . I think it will be valuable for readers who have at the same time strong interest in Bayesian decision theory and in Shannon information theory." (Michael Kohler, Metrika, Vol. 64, 2006)