Probability Models

Probability Spaces.- Conditional Probability and Independence.- Common Probability Distributions.- Random Variables.- Sums of Random Variables.- Convergence and Limit Theorems.- Stochastic Processes in Discrete Time.- Stochastic Processes in Continuous Time.- Appendix: Common Distributions and Mathematical Facts.

From the reviews: MATHEMATICAL REVIEWS " gives a concise non-measure theoretic introduction to the basics of probability theory and stochastic processes. The overall level is that of a first university course on the subject, given that the students have had introductory courses on linear algebra and real analysis. Numerous examples make the text fairly light reading the mathematically less trained reader will find the language (and terminology) used pleasant: no unnecessary pedantic notation is wasted. The more mathematically inclined reader will learn form both the examples and pedagogic line of approach. In summary, I find [this book] a useful text to have on my shelves and would consider using it as a textbook for science and engineering students. Mathematics students would benefit from it as a first contact with the world of randomness before delving deeper using a measure theoretic approach." ISI SHORT BOOK REVIEWS "What makes this book so interesting is the fact that, in only two hundred and fifty pages, the reader is brought from the very beginning to a fairly high level in the knowledge of probability theory. ... There is a wealth of about two hundred exercisis, with solutions, which makes the book useful for teaching." ISI Short Book Reviews, Vol. 22/3, December 2002 " The purpose of this book is to provide a sound introduction to the study of real-world phenomena that possess random variation . this one is a nice choice, written in a broad and lively style." (P. Schmitt, Monatshefte fur Mathematik, Vol. 143 (1), 2004) "A clear treatment of probability theory it has a great deal to recommend it. there is a real attempt to provide a readable account of the material without getting too bogged down in analytical detail and without ignoring the issues or resorting to over-simplification. It is good to have such specialized material in what is essentially a text for undergraduates, and I can recommend this stimulating book to anybody who

From the reviews of the second edition:

"It should be on the desk of any university teacher and anybody studying probability and stochastic processes. ... there are exercises given in the end of each chapter. Solutions or good hints are summarised and located at the end of the book. All these make the book more than useful to a wide spectrum of readers. For anybody needing a good introduction to modern probability and stochastic processes, this is the book to start with." (Jordan M. Stoyanov, zbMATH, Vol. 1286, 2014)

"This is an introductory book on probabilistic modeling that I can recommend to any student or teacher. It is not only for probability courses, but also for general mathematics, since the proofs, definitions, and examples are so beautifully intermingled and interspersed." (Arturo Ortiz-Tapia, Computing Reviews, November, 2013)