Multi-dimensional Monte Carlo Integrations Utilizing Mathematica (Paperback)

Sujaul Chowdhury

ISBN 13: 9798337089232

Edité par American Academic Press, 2025

A propos de cet article

Paperback. This book provides practical, hands-on demonstration of evaluation of multi-dimensional or high-dimensional definite integrals using Monte Carlo method. We have evaluated a large number of multi-dimensional definite integrals using Monte Carlo method. These are 2, 3, 5, 7 and 10 dimensional definite integrals. We have used mean value method for the evaluations. We have performed function evaluations at random values of the variables gathered as uniform random variates using inverse transform sampling method. We have performed symbolic computations using programs written in Mathematica. Very much smaller number of function evaluations is found to lead to much lower error compared to the case with uniform, non-random sampling i.e. without using Monte Carlo method. This is consistent with what we often hear about a virtue of Monte Carlo method in evaluating multi-dimensional or high-dimensional definite integrals. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

N° de réf. du vendeur 9798337089232

Détails bibliographiques

• Titre: Multi-dimensional Monte Carlo Integrations Utilizing Mathematica (Paperback)
• Éditeur: American Academic Press
• Date de parution: 2025
• Langue: anglais
• ISBN à 13 chiffres: 9798337089232
• Reliure: Paperback
• État de l'article: new

A propos de ce titre

Synopsis

This book provides practical, hands-on demonstration of evaluation of multi-dimensional or high-dimensional definite integrals using Monte Carlo method. We have evaluated a large number of multi-dimensional definite integrals using Monte Carlo method. These are 2, 3, 5, 7 and 10 dimensional definite integrals. We have used mean value method for the evaluations. We have performed function evaluations at random values of the variables gathered as uniform random variates using inverse transform sampling method. We have performed symbolic computations using programs written in Mathematica. Very much smaller number of function evaluations is found to lead to much lower error compared to the case with uniform, non-random sampling i.e. without using Monte Carlo method. This is consistent with what we often hear about a virtue of Monte Carlo method in evaluating multi-dimensional or high-dimensional definite integrals.