Essentials of Probability
James B. Bartoo, Arthur Yaspan · Technometrics · 1968
This text is essentially a progress report of the exciting work being done in thii area at Massachusetts Institute of Technology. It describes the integration of Bayesian decision theory and Markov chains, and thus builds on the work initiated by Ronald Howard and reported in his little monograph on Markov chains and dynamic programming. Like Howard’s monograph, this book was also originally a Ph.D. thesis. However, unlike Howard’s delightful and light treatment, which can be read almost like a novel, this work by Martin takes a bit of effort to plough through, since the theoretical level is significantly higher. There are no baseball games, no toy manufacturers, and no used car salesmen to brighten the journey. It is good mathematical reading, and the effort required is well worth it. This book is not a text book but the report of research. However, those in applied statistics and operations research, who are actively engaged in work in this area, will find this book to be stimulating and delightful. The author does not pretend that the methodology has immediate applicability, but it is difficult for this reviewer to believe that applications will not be found. Thus, this book is an excellent outline of a facit of mathematical research that is ripe for harvesting and it will undoubtedly stimulate much additional work in this area.