Discrete Random Variables: Functions and Distributions

Related lectures (30)

Page 3 of 3

Covers elementary distributions like Bernoulli, Binomial, Geometric, Negative Binomial, Poisson, and Multinomial.

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

Covers random variables, probability functions, expectations, variances, and joint distributions.

Explores continuous random variables, density functions, and distribution laws with practical examples.

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

Covers discrete random variables, probability mass function, properties, and binomial distribution with illustrative examples.

Explores binomial and Poisson mass functions, calculating probabilities and discussing distribution functions of random variables.

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

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Covers statistical concepts like probability, estimation, hypothesis testing, and confidence intervals for mathematicians.