Optimization and Simulation

Related lectures (31)

Covers the basics of probability distributions, including mean, variance, and properties of random variables.

Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.

Explores quantiles, sampling, and histogram density for understanding distributions and constructing confidence intervals.

Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.

Explores random variables, expectation, and independence in probability theory.

Covers the fundamentals of probability theory, including conditional probability, Bayes' rule, and random variables.

Explores discrete and continuous random variables, independence, and probability functions.

Introduces random variables, probability density functions, and Gaussian distribution in statistics.

Covers conditional density, independence of random variables, expectation, and variance calculation.

Introduces probability theory, random variables, and distributions, with a focus on their applications in atomic diffusion.

Covers random vectors, joint probability density, independent random variables, functions of two random variables, and Gaussian random variables.

Page 2 of 2