Approximately 1,000 problems (with answers and solutions at the back) illustrate such topics as random events, random variables, limit theorems, Markov processes, etc.
An Introduction to Mathematical Modeling by Edward A. Bender Accessible text features over 100 reality-based examples pulled from the science, engineering and operations research fields. Prerequisites: ordinary differential equations, continuous probability. Numerous references. Includes 27 black-and-white figures. 1978 edition.
Introduction to the Theory of Random Processes by I. I. Gikhman, A. V. Skorokhod Rigorous exposition suitable for elementary instruction. Covers measure theory, axiomatization of probability theory, processes with independent increments, Markov processes and limit theorems for random processes, more. Introduction. Bibliography. 1969 edition.
Dynamic Probabilistic Systems, Volume II: Semi-Markov and Decision Processes 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 the basic process; the second, semi-Markov and decision processes. 1971 edition.
Probability Theory by Alfred Renyi This introductory text features problems and exercises illustrating algebras of events, discrete random variables, characteristic functions, and limit theorems. An extensive appendix introduces information theory. 1970 edition.
Stationary and Related Stochastic Processes: Sample Function Properties and Their Applications by Harald Cramér, M. Ross Leadbetter This graduate-level text offers a comprehensive account of the general theory of stationary processes and develops the foundations of the general theory of stochastic processes, examines processes with a continuous-time parameter, more. 1967 edition.
