Markov chain Monte Carlo

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Related lectures (32)

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Theory of MCMC

Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.

Computer Simulation: Early Days and Monte Carlo Method

Explores the early days of computer simulation, focusing on the Monte Carlo method and its evolution in scientific research.

Markov Chain Monte Carlo: Rejection Sampling

Explores rejection sampling for generating sample values from a target distribution, along with Bayesian inference using MCMC.

Probabilistic Estimation in Spin Glass Card Game

Explores probabilistic estimation in the Spin Glass Card game, emphasizing room splitting based on a given parameter.

Monte Carlo Moves in Simulation

Explores Monte Carlo moves in simulation, including trial moves and biased moves, comparing Monte Carlo with Molecular Dynamics.

Sampling from Rough Energy Landscapes

Explores challenges and strategies for sampling from rough energy landscapes, focusing on Monte Carlo integration and tuning algorithms.

Quasi-Stationary Distribution: Molecular Dynamics Modeling

Explores the quasi-stationary distribution approach in molecular dynamics modeling, covering Langevin dynamics, metastability, and kinetic Monte Carlo models.

Monte Carlo Chain: Motivation and Algorithm

Explores the motivation and algorithm behind the Monte Carlo Chain method.

Numerical Simulation of SDEs: Monte Carlo & Optimal Control

Covers Monte Carlo methods, variance reduction, and stochastic optimal control, exploring simulation techniques, efficiency, and investment dynamics.

Monte Carlo Markov Chains

Covers the theory of Markov chains and Monte Carlo methods.