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Lecture
Information Measures: Part 2
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Related lectures (30)
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Variational Formulation: Information Measures
Explores variational formulation for measuring information content and divergence between probability distributions.
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.
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: Estimation & Detection
Covers information measures, entropy, mutual information, and data processing inequality in signal representation.
Information Measures: Part 1
Covers information measures, tail bounds, subgaussions, subpossion, independence proof, and conditional expectation.
Linear Mapping Basics
Covers the basics of linear mapping and coordinate systems.
Information Theory: Entropy and Capacity
Covers concepts of entropy, Gaussian distributions, and channel capacity with constraints.
