Stochastic Simulation: Monte Carlo Method

Related lectures (31)

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

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

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

Provides an overview of hypothesis testing and confidence intervals in statistics, including practical examples and key concepts.

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

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

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

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

Explores constructing confidence intervals and MLE limit theorems for large samples.