Informationen zum Autor Greogory F. Lawler Klappentext Focusing on mathematical ideas rather than proofs, Introduction to Stochastic Processes provides access to important fundamentals of stochastic processes. This second edition features additional material on stochastic integration, with expanded discussion of Girsanov transformation, an introduction to the Feynman-Kac formula, and an exposition on the Black-Scholes formula with applications from the field of mathematical finance. This new edition also includes new and expanded topics such as Doob's maximal inequality in the chapter on martingales and self similarity in the chapter on Brownian motion. It remains an ideal reference for professional mathematicians and statisticians as well as students. Zusammenfassung Emphasizing fundamental mathematical ideas rather than proofs, this book provides access to important foundations of probability theory applicable to problems in many fields. It also discusses Markov chains, optimal stopping, martingales, and Brownian motion. Inhaltsverzeichnis Preliminaries. Finite Markov Chains. Countable Markov Chains. Continuous-Time Markov Chains. Optimal Stopping. Martingales. Renewal Processes. Reversible Markov Chians. Brownian Motion. Stochastic Integration.

Autorentext

Greogory F. Lawler

Klappentext

Focusing on mathematical ideas rather than proofs, Introduction to Stochastic Processes provides access to important fundamentals of stochastic processes. This second edition features additional material on stochastic integration, with expanded discussion of Girsanov transformation, an introduction to the Feynman-Kac formula, and an exposition on the Black-Scholes formula with applications from the field of mathematical finance. This new edition also includes new and expanded topics such as Doob's maximal inequality in the chapter on martingales and self similarity in the chapter on Brownian motion. It remains an ideal reference for professional mathematicians and statisticians as well as students.

Zusammenfassung
Emphasizing fundamental mathematical ideas rather than proofs, this book provides access to important foundations of probability theory applicable to problems in many fields. It also discusses Markov chains, optimal stopping, martingales, and Brownian motion.

Inhalt
Preliminaries. Finite Markov Chains. Countable Markov Chains. Continuous-Time Markov Chains. Optimal Stopping. Martingales. Renewal Processes. Reversible Markov Chians. Brownian Motion. Stochastic Integration.