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Monte Carlo Methods in Scientific Computing

Monte Carlo Methods in Scientific Computing. 3-7 November 2003 Beijing International Center for Computational Physics. Outline (Monday). What is Monte Carlo Introduction to probability Random number generator Numerical integration Quasi-Monte Carlo method Markov chain. Outline (Tuesday).

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Monte Carlo Methods in Scientific Computing

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  1. Monte Carlo Methods in Scientific Computing 3-7 November 2003 Beijing International Center for Computational Physics

  2. Outline (Monday) • What is Monte Carlo • Introduction to probability • Random number generator • Numerical integration • Quasi-Monte Carlo method • Markov chain

  3. Outline (Tuesday) • Metropolis and other algorithms • Selected applications • Convergence and Monte Carlo error • Quantum Monte Carlo methods • Variational, diffusion Monte Carlo • Trotter-Suzuki formula

  4. Outline (Wednesday) • Cluster algorithms • Re-weighting methods • Extended ensemble methods (Multi-canonical, simulated tempering, replica MC, replica exchange) • Transition matrix MC, flat-histogram and Wang-Landau

  5. Thursday • Morning: Non-equilibrium dynamics in statistical mechanics and other applications (by Bo Zheng) • Afternoon: Markov chain Monte Carlo in statistics (by Junni Zhang)

  6. Friday Whole day: Monte Carlo method and its characteristics (by Pei Lucheng)

  7. Reference Books • M H Kalos and P A Whitlock, “Monte Carlo Methods”, John Wiley & Sons, 2nd ed, 2008. • D P Landau and K Binder, “A Guide to Monte Carlo Simulations in Statistical Physics”, 4th ed, Cambridge, 2015. • J S Liu, “Monte Carlo Strategies in Scientific Computing”, Springer,2002.

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