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Mean Field Simulation for Monte Carlo Integration.pdf
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Mean Field Simulation for Monte Carlo Integration
Pierre Del Moral
Chapman & Hall/CRC Monographs on Statistics & Applied Probability
Taylor and Francis
2013
In the last three decades, there has been a dramatic increase in the use of interacting particle methods as a powerful tool in real-world applications of Monte Carlo simulation in computational physics, population biology, computer sciences, and statistical machine learning. Ideally suited to parallel and distributed computation, these advanced particle algorithms include nonlinear interacting jump diffusions; quantum, diffusion, and resampled Monte Carlo methods; Feynman-Kac particle models; genetic and evolutionary algorithms; sequential Monte Carlo methods; adaptive and interacting Marko. Read more...
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