Lectures related to Statistical Models: Families and Transformations

Explores variance, covariance, and practical applications in statistics and probability.

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Covers fundamental concepts in probability and statistics, including distributions, properties, and expectations of random variables.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

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

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

Covers random variables, expectation, and distributions in probability spaces.

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.