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. A wealth of results, ideas and techniques distinguish this text. Introduction. Bibliography. 1969 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.
Applied Probability Models with Optimization Applications by Sheldon M. Ross Concise advanced-level introduction to stochastic processes that arise in applied probability. Poisson process, renewal theory, Markov chains, Brownian motion, much more. Problems. References. Bibliography. 1970 edition.
Foundations of Measurement Volume III: Representation, Axiomatization, and Invariance by Patrick Suppes, David H. Krantz, R. Duncan Luce, Amos Tversky All of the sciences have a need for quantitative measurement. This influential series established the formal foundations for measurement, justifying the assignment of numbers to objects in terms of their structural correspondence. 1990 edition.
Theory of Markov Processes by E. B. Dynkin, D. E. Brown, T. Kovary An investigation of the logical foundations of the theory behind Markov random processes, this text explores subprocesses, transition functions, and conditions for boundedness and continuity. 1961 edition.
