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This book is an illustration of the use of Monte Carlo methods applied to solve specific problems in mathematics, engineering, physics, statistics, and science in general.
In applied mathematics, the name Monte Carlo is given to the method of solving problems by means of experiments with random numbers. This name, after the casino at Monaco, was first applied around 1944 to the method of solving deterministic problems by reformulating them in terms of a problem with random elements, which could then be solved by large-scale sampling. But, by extension, the term has come to mean any simulation that uses random numbers. Monte Carlo methods have become among the most fundamental techniques of simulation in modern science.
The Paradigm of Complex Probability and Thomas Bayes’ Theorem
The Paradigm of Complex Probability and Isaac Newton’s Classical Mechanics: On the Foundation of Statistical Physics
Flooding Fragility Model Development Using Bayesian Regression
Markov Chain Monte Carlo in a Dynamical System of Information Theoretic Particles
Monte Carlo and Medical Physics
Reliability and Comparison of Some GEANT4-DNA Processes and Models for Proton Transportation: An Ultra-Thin Layer Study
Applications of Simulation Codes Based on Monte Carlo Method for Radiotherapy
Physical Only Modes Identification Using the Stochastic Modal Appropriation Algorithm