Monte Carlo Methods: History and Applications

Thomas B. Hall (Editor)

Series: Mathematics Research Developments
BISAC: MAT042000, MAT029000



Volume 10

Issue 1

Volume 2

Volume 3

Special issue: Resilience in breaking the cycle of children’s environmental health disparities
Edited by I Leslie Rubin, Robert J Geller, Abby Mutic, Benjamin A Gitterman, Nathan Mutic, Wayne Garfinkel, Claire D Coles, Kurt Martinuzzi, and Joav Merrick


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In this compilation, the authors first consider applying the Monte Carlo method to the general form of the heat equation that is used for analyzing conduction heat transfer. The Monte Carlo method is then extended to some convection heat transfer applications by representing the probabilistic interpretation of the energy equation to obtain the temperature profile.

Following this, Monte Carlo Methods: History and Applications discusses the Monte Carlo methods needed for the estimation of the mean glandular dose in both digital mammography and digital breast tomosynthesis. Various breast anatomies are considered.

The gradual development of the Monte Carlo method for solving problems of mathematical chemistry is considered. A comparison of various quantitative structure–property/activity relationships based on the Monte Carlo method is also presented.

Lastly, the Monte Carlo technique is used to characterize the statistical distributions of received measurements in an electric energy power system, as well as to quantify the correlations among these variables. To check the numerical accuracy of the results, the point estimate algorithm is employed.
(Imprint: Nova)


Chapter 1. Monte Carlo Methods for Heat Transfer
(Hooman Naeimi, Department of Mechanical Engineering, University of Bojnord, Bojnord, North Khorasan, Iran)

Chapter 2. Monte Carlo Methods to Evaluate the Mean Glandular Dose in Mammography and Digital Breast Tomosynthesis
(R. M. Tucciariello, P. Barca, R. Lamastra, A.C. Traino and M. E. Fantacci, Department of Physics, University of Pisa, Pisa, Italy, and others)

Chapter 3. Use of the Monte Carlo Method to Build up QSPR/QSAR Models: Index of Ideality of Correlation and Correlation Intensity Index
(Alla P. Toropova and Andrey A. Toropov, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy)

Chapter 4. Application of Monte Carlo Method to Electric Power Systems: Computation of Measurement Correlations
(E. Caro, Laboratory of Statistics, ETSII, Universidad Politécnica de Madrid, Madrid, Spain)


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