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Introduction to Monte-Carlo Methods for Transport and Diffusion Equations
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Introduction to Monte-Carlo Methods for Transport and Diffusion Equations

Book Details

Format Hardback or Cased Book
ISBN-10 0198525923
ISBN-13 9780198525929
Publisher Oxford University Press
Imprint Oxford University Press
Country of Manufacture GB
Country of Publication GB
Publication Date Jul 24th, 2003
Print length 174 Pages
Weight 396 grams
Dimensions 16.50 x 24.40 x 1.70 cms
Ksh 11,800.00
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This text is aimed at graduate students in mathematics, physics, engineering, economics, finance, and the biosciences that are interested in using Monte-Carlo methods for the resolution of real-life scenarios.
Monte-Carlo methods is the generic term given to numerical methods that use sampling of random numbers. This text is aimed at graduate students in mathematics, physics, engineering, economics, finance, and the biosciences that are interested in using Monte-Carlo methods for the resolution of partial differential equations, transport equations, the Boltzmann equation and the parabolic equations of diffusion. It includes applied examples, particularly in mathematical finance, along with discussion of the limits of the methods and description of specific techniques used in practice for each example.This is the sixth volume in the Oxford Texts in Applied and Engineering Mathematics series, which includes texts based on taught courses that explain the mathematical or computational techniques required for the resolution of fundamental applied problems, from the undergraduate through to the graduate level. Other books in the series include: Jordan & Smith: Nonlinear Ordinary Differential Equations: An introduction to Dynamical Systems; Sobey: Introduction to Interactive Boundary Layer Theory; Scott: Nonlinear Science: Emergence and Dynamics of Coherent Structures; Tayler: Mathematical Models in Applied Mechanics; Ram-Mohan: Finite Element and Boundary Element Applications in Quantum Mechanics; Elishakoff and Ren: Finite Element Methods for Structures with Large Stochastic Variations.

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