Probabilities: Fundamentals and Independence

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Related lectures (29)

Entropy and Data Compression: Huffman Coding Techniques

Discusses entropy, data compression, and Huffman coding techniques, emphasizing their applications in optimizing codeword lengths and understanding conditional entropy.

Conditional Expectation: Basics

Introduces the basics of conditional expectation, covering definitions, properties, and examples in the context of random variables.

Central Limit Theorem: Properties and Applications

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Introduction to Inference

Covers the basics of probability theory, random variables, joint probability, and inference.

Conditional Expectation Properties

Explores conditional expectation properties, including measurability, linearity, and independence of random variables.

Convergence in Probability

Explores convergence in probability, concentration inequalities, laws of large numbers, and properties of distributions.

Law of Total Probability

Explores the Law of Total Probability and its applications in real-world scenarios, introducing key concepts in probability theory.

Conditional Probability Distributions

Covers conditional probability distributions and introduces the concept of conditional expected value.

Probability and Statistics

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

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