Stochastic Processes (Cambridge Series in Statistical and Probabilistic Mathematics, 33)

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Product Description

This comprehensive guide to stochastic processes gives a complete overview of the theory and addresses the most important applications. Pitched at a level accessible to beginning graduate students and researchers from applied disciplines, it is both a course book and a rich resource for individual readers. Subjects covered include Brownian motion, stochastic calculus, stochastic differential equations, Markov processes, weak convergence of processes and semigroup theory. Applications include the Black-Scholes formula for the pricing of derivatives in financial mathematics, the Kalman-Bucy filter used in the US space program and also theoretical applications to partial differential equations and analysis. Short, readable chapters aim for clarity rather than full generality. More than 350 exercises are included to help readers put their new-found knowledge to the test and to prepare them for tackling the research literature.

Stochastic Processes (Cambridge Series in Statistical and Probabilistic Mathematics, 33) Review

In 42 short chapters the book covers many different stochastic processes. At times, one wouldvlike to know a bit more about the topic covered in a chapter - but then the book would probably have become too large.
Really good knowledge of measure theory ( which I do not have) and some knowledge of probability theory definitely helps understanding the book, at least for some of the chapters.

The proofs are generally not too difficult, sometimes a few more lines would have helped at least my understanding. (However, I am an engineer, not a mathematician). Best I can tell, they are rigorous.

An appendix gives some probability theory - but if one has no prior knowledge in this, this appendix is not enough to understand the book.

The book contains a 'normal' amount of typos, neither good nor very bad. Most of them are harmless and do not hinder the understanding.

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