Introduction to stochastic processes

By:

Chen, Mu-Fa

Contributor(s):

Mao, Yong-Hua

Material type: Text

TextSeries: World Scientific series on probability theory and its applications, v. 2Publication details: Higher Education Press Limited Company 2023 BeijingDescription: 230 pISBN:

9781944660512

Subject(s):

Stochastic processes

DDC classification:

519.2 CHE

Summary: This book introduces stochastic processes in a concise manner, focusing on Markov chains and stochastic analysis. It covers ergodicity, recurrence, and various types of ergodicity using modern techniques like coupling and duality methods. The book also covers martingale theory, Brownian motions, stochastic integral, stochastic differential equations, and multidimensional stochastic integral and equations. It introduces topics like the Feynman-Kac formula, random time transform, and Girsanov transform, and the Brunn-Minkowski inequality in convex geometry. The book also features modern probability theory used in fields like MCMC and convex geometry and number theory.

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	Jammu General Stacks	Non-fiction	519.2 CHE (Browse shelf(Opens below))	Available		IIMJ-8949

Total holds: 0

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1. Discrete-Time Markov Chains 2. Continuous-Time Markov Chains 3. Reversible Markov Chains 4. General Markov Processes 5. Martingale 6. Brownian Motion 7. Stochastic Integral and Diffusion Processes 8. Semimartingale and Stochastic Integral

This book introduces stochastic processes in a concise manner, focusing on Markov chains and stochastic analysis. It covers ergodicity, recurrence, and various types of ergodicity using modern techniques like coupling and duality methods. The book also covers martingale theory, Brownian motions, stochastic integral, stochastic differential equations, and multidimensional stochastic integral and equations. It introduces topics like the Feynman-Kac formula, random time transform, and Girsanov transform, and the Brunn-Minkowski inequality in convex geometry. The book also features modern probability theory used in fields like MCMC and convex geometry and number theory.

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