Information and Coding Theory
Explore information measures; compression and channels; error correction. Concepts and connections are available independently of video coverage.
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Hamming distance counts differing positions between equal-length strings.
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Minimum distance d permits correcting up to floor((d-1)/2) arbitrary symbol errors.
Codewords forming a finite-field vector subspace enable algebraic encoding and decoding.
A probability measure assigns nonnegative weights to events, with total weight one and countable additivity on disjoint events.
Source coding represents messages efficiently, with entropy constraining lossless average code length.
Capacity bounds achievable reliable information rates for a specified channel model.
Explore information measures; compression and channels; error correction. Concepts and connections are available independently of video coverage.