Monte Carlo Strategies in Scientific Computing
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Beschreibung
Introduction and Examples.- Basic Principles: Rejection, Weighting, and Others.- Theory of Sequential Monte Carlo.- Sequential Monte Carlo in Action.- Metropolis Algorithm and Beyond.- The Gibbs Sampler.- Cluster Algorithms for the Ising Model.- General Conditional Sampling.- Molecular Dynamics and Hybrid Monte Carlo.- Multilevel Sampling and Optimization Methods.- Population-Based Monte Carlo Methods.- Markov Chains and Their Convergence.- Selected Theoretical Topics.- Basics in Probability and Statistics.- References.- Author Index.- Subject Index.
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