Description: Developed from the author’s course at the Ecole Polytechnique, Monte-Carlo Methods and Stochastic Processes: From Linear to Non-Linear focuses on the simulation of stochastic processes in continuous time and their link with partial differential equations (PDEs). It covers linear and nonlinear problems in biology, finance, geophysics, mechanics, chemistry, and other application areas. The text also thoroughly develops the problem of numerical integration and computation of expectation by the Monte-Carlo method.The book begins with a history of Monte-Carlo methods and an overview of three typical Monte-Carlo problems: numerical integration and computation of expectation, simulation of complex distributions, and stochastic optimization. The remainder of the text is organized in three parts of progressive difficulty. The first part presents basic tools for stochastic simulation and analysis of algorithm convergence. The second part describes Monte-Carlo methods for the simulation of stochastic differential equations. The final part discusses the simulation of non-linear dynamics.
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Location: Waco, Texas
End Time: 2024-11-21T21:18:23.000Z
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Item Specifics
All returns accepted: ReturnsNotAccepted
Educational Level: University
Level: Advanced
Country/Region of Manufacture: United States
Number of Pages: 310 Pages
Language: English
Publication Name: Monte-Carlo Methods and Stochastic Processes : from Linear to Non-Linear
Publisher: CRC Press LLC
Publication Year: 2016
Subject: Probability & Statistics / Stochastic Processes, Probability & Statistics / Bayesian Analysis
Item Height: 0.9 in
Item Weight: 21.7 Oz
Type: Textbook
Subject Area: Mathematics
Item Length: 9.4 in
Author: Emmanuel Gobet
Item Width: 6.4 in
Format: Hardcover
