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Related lectures (32)

Page 2 of 4

Statistical Theory: Inference and Optimality

Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.

Confidence Intervals: Margins, Coverage, Pivots

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

Confidence Interval for Bernoulli Model Score

Explains how to calculate the confidence interval for the score parameter in the Bernoulli model.

Basic Concepts of Statistics: Weighing Three Objects

Covers basic statistics concepts, confidence intervals, t distribution, and experimental study strategies.

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.

Variance Reduction Techniques

Covers variance reduction techniques in stochastic simulation to improve output quantity estimation accuracy.

Confidence Intervals: Gaussian Estimation

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

Modeling Elasticity and Coefficients

Explores correlation matrices, regression, variance, confidence intervals, and standardized systems in statistical modeling.

Multi-arm Bandits: Upper Confidence Bound

Covers the Upper Confidence Bound algorithm for multi-arm bandits.

Hypothesis Testing: A Different Perspective

Delves into a different perspective on hypothesis testing, emphasizing the p-value and significance levels.