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Lecture
Variational Formulation: Information Measures
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Related lectures (31)
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Information Measures: Entropy and Information Theory
Explains how entropy measures uncertainty in a system based on possible outcomes.
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.
Lecture: Shannon
Covers the basics of information theory, focusing on Shannon's setting and channel transmission.
Mutual Information and Entropy
Explores mutual information and entropy calculation between random variables.
Information Measures
Covers information measures like entropy and Kullback-Leibler divergence.
Mutual Information: Understanding Random Variables
Explores mutual information, quantifying relationships between random variables and measuring information gain and statistical dependence.
Information Measures: Part 2
Covers information measures like entropy, joint entropy, and mutual information in information theory and data processing.
Information Measures: Estimation & Detection
Covers information measures, entropy, mutual information, and data processing inequality in signal representation.
Information Measures
Covers variational representation and information measures such as entropy and mutual information.
