Concept

Monte Carlo method

Related lectures (32)

Covers Markov chains, Monte Carlo sampling, isotropy, and the curse of dimensionality.

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.

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.

Max Entropy and Monte Carlo

Explores max entropy, Shannon's entropy, Lagrange multipliers, and Monte Carlo sampling techniques.

Monte-Carlo Integration: Approximation and Variance

Explores Monte-Carlo integration for approximating expectations and variances using random sampling and discusses error components in conditional choice models.

Molecular Dynamics and Monte Carlo

Covers computational methods for molecular systems at finite temperature, emphasizing stochastic sampling and time evolution simulations.

Diffusion Approximation: RTE

Explores the Diffusion Approximation method for solving the RTE in tissue optics, emphasizing its limitations and applications, along with the practical aspects of Monte Carlo simulations.

Sampling from Rough Energy Landscapes

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

Random Processes and Monte Carlo Simulation

Explores random processes with given probabilities and Monte Carlo simulation, emphasizing the Metropolis algorithm and stochastic matrices.

Biased Monte Carlo Markov Chain

Explores Biased Monte Carlo Markov Chain, including Bayes-optimal estimation and Metropolis-Hastings algorithm.

Markov Chain Monte Carlo

Explains the Markov Chain Monte Carlo method and the Metropolis-Hastings algorithm for sampling.

Monte Carlo Methods: Metropolis Algorithm

Explores Monte Carlo methods and the Metropolis algorithm for efficient simulations in complex systems.

Discrepancy Function: Estimation and Applications

Explores estimating discrepancy functions and their practical applications in generating sets with low discrepancy.

Monte Carlo Integration: Correlated Sampling

Covers Monte Carlo integration through correlated sampling and error scaling for grid integration.

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.

Stochastic Simulation: Monte Carlo Method

Covers the properties and error estimates of the Monte Carlo method in stochastic simulation.

Policy Estimation: The Log-Likelihood Trick

Explores the log-likelihood trick and policy gradient estimation for updating policy weights based on rewards and fast gradient approximations.

Monte Carlo Moves in Simulation

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

Stochastic Simulation: Introduction and G/G/1 Queueing Model

Covers the stochastic simulations course, G/G/1 queueing model, computational finance, statistics, physics, and Bayesian inference.

Molecular Dynamics and Monte Carlo: Computational Environment

Covers the computational environment for Molecular Dynamics and Monte Carlo exercises, emphasizing theoretical understanding over coding skills.