Introduction to Stochastic Processes (2nd Ed.) Chapman & Hall/CRC Probability Series
Auteur : Lawler Gregory F.
Emphasizing fundamental mathematical ideas rather than proofs, Introduction to Stochastic Processes, Second Edition provides quick access to important foundations of probability theory applicable to problems in many fields. Assuming that you have a reasonable level of computer literacy, the ability to write simple programs, and the access to software for linear algebra computations, the author approaches the problems and theorems with a focus on stochastic processes evolving with time, rather than a particular emphasis on measure theory.
For those lacking in exposure to linear differential and difference equations, the author begins with a brief introduction to these concepts. He proceeds to discuss Markov chains, optimal stopping, martingales, and Brownian motion. The book concludes with a chapter on stochastic integration. The author supplies many basic, general examples and provides exercises at the end of each chapter.
New to the Second Edition:
Applicable to the fields of mathematics, statistics, and engineering as well as computer science, economics, business, biological science, psychology, and engineering, this concise introduction is an excellent resource both for students and professionals.
Date de parution : 05-2006
15.6x23.4 cm
Thèmes d’Introduction to Stochastic Processes :
Mots-clés :
Random Variable T1; markov; Optional Sampling Theorem; chain; Discrete Time Markov Chains; random; Continuous Time Markov Chain; variable; Conditional Expectation; simple; Simple Random Walk; walk; Set S1; continuous; Markov Chain; time; Irreducible Markov Chain; state; Recurrent Chain; space; Superharmonic Function; Standard Brownian Motion; Brownian Motion; Invariant Probability Distribution; Customer Arrives; Invariant Probability; Positive Recurrent; Stochastic Integral; Transition Matrix; Residual Life Distribution; Extinction Probability; Uniformly Integrable; Irreducible Continuous Time Markov Chain; Distribution Function; Invariant Probability Vector