Random Vectors: Stochastic Models for Communications

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 1 of 4

Random Vectors and Stochastic Models for Communications

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

Stochastic Models for Communications

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

Elements of Statistics

Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.

Probability and Statistics: Fundamentals

Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.

Elements of Statistics: Probability and Random Variables

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

Probability Theory: Midterm Solutions

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

Conditional Density and Expectation

Explores conditional density, expectations, and independence of random variables with practical examples.

Probability and Statistics

Covers probability, statistics, independence, covariance, correlation, and random variables.

Statistical Theory: Fundamentals

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

Probability and Statistics: Independence and Conditional Probability

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.