This book is an integrated work published in two volumes. The first volume treats the basic Markov process and its variants; the second, semi-Markov and decision processes. It equips readers to formulate, analyze, and evaluate simple and advanced Markov models of systems, ranging from genetics to space engineering to marketing. 1971 edition. Reprint of the John Wiley & Sons, New York, 1971 edition.
Dynamic Probabilistic Systems, Volume I: Markov Models by Ronald A. Howard An integrated work in two volumes, this text teaches readers to formulate, analyze, and evaluate Markov models. The first volume treats basic process; the second, semi-Markov and decision processes. 1971 edition.
Probability Theory: A Concise Course by Y. A. Rozanov This clear exposition begins with basic concepts and moves on to combination of events, dependent events and random variables, Bernoulli trials and the De Moivre-Laplace theorem, and more. Includes 150 problems, many with answers.
Dynamic Programming: Models and Applications by Eric V. Denardo Introduction to sequential decision processes covers use of dynamic programming in studying models of resource allocation, methods for approximating solutions of control problems in continuous time, production control, more. 1982 edition.
