Probability & Stochastic Processes

Related lectures (28)

Page 1 of 3

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Explores rejection sampling for generating sample values from a target distribution, along with Bayesian inference using MCMC.

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Covers the estimation of distributions using samples and probability models.

Explores continuous random variables and their properties, including support and cumulative distribution functions.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Explores joint random variables, conditional density, and independence in probability and statistics.