Importance sampling

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Related lectures (8)

Monte Carlo Methods: Metropolis Algorithm

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

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 Simulations: Photon Gas & LJ Potential

Covers importance sampling, detailed balance, and Metropolis Monte Carlo simulations in photon gas and LJ systems.

Variance Reduction Techniques

Covers variance reduction techniques in stochastic simulation to improve accuracy and efficiency.

Monte Carlo Simulations

Covers Monte Carlo simulations, ensemble properties, and numerical integration techniques.

Numerical Integration Algorithms

Explores numerical integration algorithms, configurational space integrals, Maxwell-Boltzmann Distribution, and importance sampling in ensemble averages.

Sampling from Rough Energy Landscapes

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

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.

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