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, a detailed treatment of Markov chains, continuous Markov processes, and more. Includes 150 problems, many with answers. Indispensable to mathematicians and natural scientists alike.
Here's a sample of other books in this Dover category
Probability: Elements of the Mathematical Theory by C. R. Heathcote Text deals with basic notions of probablity spaces, random variables, distribution and generating functions, joint distributions and the convergence properties of sequences of random variables. Over 250 exercises with solutions.
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.
Information Theory by Robert B. Ash Analysis of channel models and proof of coding theorems; study of specific coding systems; and study of statistical properties of information sources. Sixty problems, with solutions. Advanced undergraduate to graduate level.
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.
Good Thinking: The Foundations of Probability and Its Applications by Irving John Good This in-depth treatment of probability theory by a famous British statistician explores Keynesian principles and surveys such topics as Bayesian rationality, corroboration, hypothesis testing, and mathematical tools for induction and simplicity. 1983 edition.
Basic Probability Theory by Robert B. Ash This text emphasizes the probabilistic way of thinking, rather than measure-theoretic concepts. Geared toward advanced undergraduates and graduate students, it features solutions to some of the problems. 1970 edition.
Foundations of Probability by Alfred Renyi Taking an innovative approach to both content and methods, this book explores the foundations, basic concepts, and fundamental results of probability theory, plus mathematical notions of experiments and independence. 1970 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.
Finite Markov Processes and Their Applications by Marius Iosifescu Self-contained treatment covers both theory and applications. Topics include the fundamental role of homogeneous infinite Markov chains in the mathematical modeling of psychology and genetics. 1980 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.
Foundations of Measurement Volume II: Geometrical, Threshold, and Probabilistic Representations by David H. Krantz, R. Duncan Luce, Amos Tversky, Patrick Suppes 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. 1989 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.
