Stochastic Simulation: Low-Discrepancy Point Sets

Related lectures (32)

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

Delves into linear regression, emphasizing least squares estimation, residuals, and variance.

Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.

Introduces Fisher Information for parameter estimation based on collected data.

Covers the concepts of parameter estimation, including unbiased estimators and Fisher information.

Explores hypothesis testing using Wilks' Theorem, likelihood ratio statistics, p-values, interval estimation, and confidence regions.

Covers point estimation, confidence intervals, and hypothesis testing for smooth functions using mixed models and spline smoothing.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Explains margins of error, coverage, and pivots in constructing confidence intervals for scalar parameters.

Covers confidence intervals, R2, and examples on cement heat evolution and car horsepower-MPG relationships.

Covers error estimation in Latin Hypercube Sampling, emphasizing the importance of accurate variance estimation.

Explores the linear regression model, OLS properties, hypothesis testing, interpretation, transformations, and practical considerations.

Page 2 of 2