Monte Carlo Moves in Simulation

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Covers the basic concepts of Quantum Monte Carlo and its application in spin chains, Markov chains, and Monte Carlo simulations.

Markov Chains and Applications

Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.

Simulation & Optimization: Poisson Process & Random Numbers

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.

Statistical Physics: Systems Isolation

Explores statistical physics concepts in isolated systems, focusing on entropy and disorder.

Monte Carlo Techniques: Sampling and Simulation

Explores Monte Carlo techniques for sampling and simulation, covering integration, importance sampling, ergodicity, equilibration, and Metropolis acceptance.

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Markov Chains: Ergodicity and Stationary Distribution

Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.

Stochastic Simulation: Markov Chains and Monte Carlo

Covers Markov chains, Monte Carlo methods, low discrepancy sequences, and multidimensional integrals computation.

Optimization and Simulation: Bayesian Inference

Explores Bayesian inference, knapsack problem, and prediction using Markov Chain Monte Carlo methods.

Molecular Dynamics Basics

Covers the basics of Molecular Dynamics, including Verlet integrator and periodic boundary conditions.