Monte Carlo methods

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MonteCarloPicture


The objective of this Course is to provide an introduction to Monte Carlo methods, which are probabilistic computational techniques with a wide and growing range of applications.

Prerequisites: Students should be familiar with basic probability and, for practical applications, programming.

Main topics:

1. Randomized vs Deterministic Algorithms: The curse of dimensionality.

  • Uniform, Importance and Rejection Sampling

2. Markov Chain Monte Carlo

  • Metropolis and heat-bath algorithms

3. Dynamical slowing down

  • Cluster and faster than the clock algorithms

4. Exact sampling

  • Coupling from the past

5. Frustration and optimization

  • Annealing and Tempering

6. Dynamically arrested states

  • Sampling blocked configurations

7. Non-equilibrium steady states

  • Direct evaluation of large-deviations function

References:

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