Login to filter by course Login to filter by course Reset
Introduces subgaussian and subexponential random variables, conditional expectation, and Orlicz norms.
Explores importance sampling through a change of variable to speed up Monte Carlo calculations and discusses the impact on stochastic estimates and variance scaling.
Covers continuous random variables, probability density functions, and distributions, with practical examples.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers the basics of discrete probability, including notations, axioms, pmf, examples, expectation, variance, and indicator variables.
Explores hypothesis testing in statistics, focusing on decision-making based on sample data and controlling error probabilities.
Introduces the basics of conditional expectation, covering definitions, properties, and examples in the context of random variables.
