Probability Theory: Random Variables and Distributions

Related lectures (29)

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Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

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

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Introduces random variables, probability distributions, and expected values through practical examples.

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

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

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

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

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

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