Probability Theory: Basics and Applications

Related lectures (29)

Page 3 of 3

Covers the normal distribution, including its properties and calculations.

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

Introduces continuous random variables and their probability distributions, emphasizing their applications in statistics and data science.

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

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.

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

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

Covers the basics of Probability Density Function (PDF) in probability theory.

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.