Numerical Simulation of SDEs: Monte Carlo & Optimal Control

Related lectures (32)

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Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Explores optimization and estimation in Monte Carlo methods, emphasizing Bayes-optimal groups and estimators.

Explores the MSE quality measure for estimators and the bias-variance tradeoff.

Covers the Monte Carlo method for generating realizations and unbiased estimators.

Explores confidence intervals, Gaussian estimation, Cramér-Rao inequality, and Maximum Likelihood Estimators.

Explores point estimation methods like MOM and MLE, discussing bias, variance, and examples.

Explores statistical analysis, mean square error, and bootstrapping methods in optimization and simulation.

Covers the Monte Carlo method for statistical inferences using simulation tools and sample mean estimators.

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