Numerical Simulation of SDEs: Monte Carlo & Optimal Control

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Explores probabilistic estimation in the Spin Glass Card game, emphasizing room splitting based on a given parameter.

Covers criteria for estimating parameters, emphasizing the importance of consistency, bias, variance, and efficiency of estimators.

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

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

Explores the distribution theory of least squares estimators in a Gaussian linear model, focusing on precision and confidence intervals construction.

Covers hypothesis testing, power, confidence intervals, and small sample considerations.

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

Explores efficient stochastic numerical methods for modeling and learning, covering topics like the Analytical Engine and kinase inhibitors.

Introduces linear regression concepts, from X-bands creation to slope estimator properties and tests.

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