Lectures in course COM-300: Stochastic models in communication

Stochastic Models for Communications

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

Stochastic Models for Communications: Markov Chains and Random Variables

Covers Markov chains, random variables, independence, characteristic functions, and queueing theory.

Probability Theory: Basics and Applications

Covers the fundamentals of probability theory, including corollaries, conditional probability, total probability theorem, and random variables.

Stochastic Models for Communications

Explores stochastic models for communications, covering mean, variance, characteristic functions, inequalities, various discrete and continuous random variables, and properties of different distributions.

Stochastic Models for Communications

Covers mathematical tools for communication systems and data science, including information theory and signal processing.

Little's Law: Understanding System Performance

Explores Little's Law, a key concept in system analysis and queuing theory.

Arrival and Departure Processes

Covers arrival and departure processes, Poisson distributions, service times, and system performance evaluation.