Concise advanced-level introduction to stochastic processes that frequently arise in applied probability. Largely self-contained text covers Poisson process, renewal theory, Markov chains, inventory theory, Brownian motion and continuous time optimization models, much more. Problems and references at chapter ends. "excellent introduction." — Journal of the American Statistical Association. Bibliography. 1970 edition.
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.
Optimization Theory with Applications by Donald A. Pierre Broad-spectrum approach to important topic. Explores the classic theory of minima and maxima, classical calculus of variations, simplex technique and linear programming, optimality and dynamic programming, more. 1969 edition.
Fifty Challenging Problems in Probability with Solutions by Frederick Mosteller Remarkable puzzlers, graded in difficulty, illustrate elementary and advanced aspects of probability. These problems were selected for originality, general interest or because they demonstrate valuable techniques. Also includes detailed solutions.
Mathematical Modelling Techniques by Rutherford Aris Highly useful volume discusses the types of models, how to formulate and manipulate it for best results. Numerous examples.
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.
Gaussian Processes, Function Theory, and the Inverse Spectral Problem by H. Dym, H. P. McKean This text offers background in function theory, Hardy functions, and probability as preparation for surveys of Gaussian processes, strings and spectral functions, and strings and spaces of integral functions. 1976 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.
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.
An Introduction to Identification by J. P. Norton Suitable for advanced undergraduates and graduate students, this text covers the theoretical basis for mathematical modeling as well as a variety of identification algorithms and their applications. 1986 edition.
Nonstandard Methods in Stochastic Analysis and Mathematical Physics by Sergio Albeverio, Jens Erik Fenstad, Raphael Høegh-Krohn, Tom Lindstrøm Two-part treatment begins with a self-contained introduction to the subject, followed by applications to stochastic analysis and mathematical physics. "A welcome addition." — Bulletin of the American Mathematical Society. 1986 edition.
Analytical Methods of Optimization by D. F. Lawden Suitable for advanced undergraduates and graduate students, this text surveys the classical theory of the calculus of variations. Topics include static systems, control systems, additional constraints, the Hamilton-Jacobi equation, and the accessory optimization problem. 1975 edition.
Introduction to Stochastic Models: Second Edition by Roe Goodman Newly revised by the author, this undergraduate-level text introduces the mathematical theory of probability and stochastic processes. Features worked examples as well as exercises and solutions.
Optimal Control Theory: An Introduction by Donald E. Kirk Upper-level undergraduate text introduces aspects of optimal control theory: dynamic programming, Pontryagin's minimum principle, and numerical techniques for trajectory optimization. Numerous figures, tables. Solution guide available upon request. 1970 edition.
Stochastic Models in Operations Research, Vol. II: Stochastic Optimization by Daniel P. Heyman, Matthew J. Sobel The 2nd of a graduate-level 2-volume set introduces myopic optimal policies, Markov decision processes, generalizations of MDPs and related computational considerations, monotone optimal policies, and sequential games. 1984 edition. Includes 43 figures and 50 tables.
