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This compendium describes how Monte Carlo methods can be applied to simulate technical systems. The description covers background on probability theory and random number generation as well as the thoery and practice of efficient Monte Carlo simulations.
Objectives of the Course. Introduce the main tools for the simulation of random variables and the approximation of multidimensional integrals: Integration by Monte Carlo, inversion method, transformation method, rejection sampling, importance sampling,
Monte Carlo method is a (computational) method that relies on the use of random sampling and probability statistics to obtain numerical results for solving deterministic or probabilistic problems.
These notes are intended as an introduction to Monte Carlo methods in physics with an emphasis on Markov chain Monte Carlo and critical phe-nomena. Some simple stochastic models are also introduced; many of them have been selected because of there interesting collective behavior. The term
Chapter 1 provides an introduction to Monte Carlo methods and applications. The different classes of dynamic models that are encountered in simulation are outlined, and due emphasis is placed on pitfalls and limitations of Monte Carlo methods. Chapter 2 deals with numerical integration meth-ods.
Handbook of Monte Carlo Methods. WILEY SERIES IN PROBABILITY AND STATISTICS. Established by WALTER A. SHEWHART and SAMUEL S. WILKS.
Monte Carlo methods for radiation transport. Fermi (1930): random method to calculate the properties of the newly discovered neutron. Manhattan project (40’s): simulations during the initial development of thermonuclear weapons. von Neumann and Ulam coined the term “Monte Carlo”.
