Multivariate random variable

Related lectures (30)

Page 3 of 3

Covers random vectors, joint probability, and conditional probability in communication stochastic models.

Covers Linearity of Expectation, Markov's inequality, random variables, and transitive tournaments.

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

Explores probabilistic analysis of the hiring problem using Indicator Random Variables and clever methods to calculate expected outcomes.

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

Explores filter synthesis methods and the role of random variables in measurement systems.

Covers advanced probability concepts, including random variables and expected value calculation.

Explores covariance, correlation, and mutual information in quantifying statistical dependence between random variables.

Covers random vectors, joint probability, and Gaussian random variables in communication models.

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.