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Lecture
Maximal Correlation: Information Measures
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Related lectures (32)
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Information Measures: Entropy and Information Theory
Explains how entropy measures uncertainty in a system based on possible outcomes.
Variational Formulation: Information Measures
Explores variational formulation for measuring information content and divergence between probability distributions.
Information Measures
Covers information measures like entropy, Kullback-Leibler divergence, and data processing inequality, along with probability kernels and mutual information.
Information Theory: Review and Mutual Information
Reviews information measures like entropy and introduces mutual information as a measure of information between random variables.
Information Measures: Part 2
Covers information measures like entropy, joint entropy, and mutual information in information theory and data processing.
Mutual Information and Entropy
Explores mutual information and entropy calculation between random variables.
Information Measures: Estimation & Detection
Covers information measures, entropy, mutual information, and data processing inequality in signal representation.
Mutual Information: Understanding Random Variables
Explores mutual information, quantifying relationships between random variables and measuring information gain and statistical dependence.
Generalization Error
Discusses mutual information, data processing inequality, and properties related to leakage in discrete systems.
Information Measures: Part 1
Covers information measures, tail bounds, subgaussions, subpossion, independence proof, and conditional expectation.
