Data Compression and Shannon's Theorem: Entropy Calculation Example

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Explores the Source Coding Theorem, entropy, Huffman coding, and conditioning's impact on entropy reduction.

Introduces random variables and their significance in information theory, covering concepts like expected value and Shannon's entropy.

Explores the relationship between code word length and probability distribution, focusing on designing prefix-free codes for efficient compression.

Covers source coding theorems, entropy, and various source models in information theory.

Covers the basics of information theory, focusing on Shannon's setting and channel transmission.

Introduces Shannon's Theorem on binary codes, entropy, and data compression limits.

Reviews information measures like entropy and introduces mutual information as a measure of information between random variables.

Explores data compression, including lossless methods and the necessity of lossy compression for real numbers and signals.

Covers entropy, source coding, encoding maps, decodability, prefix-free codes, and Kraft-McMillan's inequality.

Explores stationary sources, entropy, regularity, and coding efficiency, including a challenging problem with billiard balls.