Joint entropy

Applied sciences
Information engineering
Information theory
Channel capacity

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Related lectures (10)

Information Measures: Entropy and Information Theory

Explains how entropy measures uncertainty in a system based on possible outcomes.

Mutual Information and Entropy

Explores mutual information and entropy calculation between random variables.

Conditional Entropy: Huffman Coding

Explores conditional entropy and Huffman coding for efficient data compression techniques.

Conditional Entropy: Review and Definitions

Covers conditional entropy, weather conditions, function entropy, and the chain rule.

Entropy Bounds: Conditional Entropy Theorems

Explores entropy bounds, conditional entropy theorems, and the chain rule for entropies, illustrating their application through examples.

Information Measures: Part 2

Covers information measures like entropy, joint entropy, and mutual information in information theory and data processing.

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 Entropy and Data Compression Techniques

Discusses conditional entropy and its role in data compression techniques.

Conditional Entropy and Information Theory Concepts

Discusses conditional entropy and its role in information theory and data compression.

Information Theory: Entropy and Capacity

Covers concepts of entropy, Gaussian distributions, and channel capacity with constraints.

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