TI-83 series

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Related lectures (15)

Information Theory and Coding

Covers source coding, Kraft's inequality, mutual information, Huffman procedure, and properties of tropical sequences.

Source Coding and Prefix-Free Codes

Covers source coding, injective codes, prefix-free codes, and Kraft's inequality.

Information Theory and Coding

Covers expected code word length, Huffman procedure, and entropy in coding theory.

Data Compression: Source Coding

Covers data compression techniques, including source coding and unique decodability concepts.

Universal Compression: Lempel-Ziv Method

Covers the Universal Compression using the Lempel-Ziv method and demonstrates its superiority over other methods.

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Universal Source Coding

Covers the Lempel-Ziv universal coding algorithm and invertible finite state machines in information theory.

Information Theory: Entropy and Information Processing

Explores entropy in information theory and its role in data processing and probability distributions.

Information Theory: Channel Capacity and Convex Functions

Explores channel capacity and convex functions in information theory, emphasizing the importance of convexity.

Information Theory: Source Coding & Channel Coding

Covers the fundamentals of information theory, focusing on source coding and channel coding.

Continuous-Time Markov Chains: Kolmogorov Equations

Covers continuous-time Markov chains and Kolmogorov equations in stochastic communication models.

Continuous-Time Markov Chains: Kolmogorov Equations

Covers the equations of Kolmogorov for continuous-time Markov chains.

Operator Matrix Elements

Explores the physical meaning of operator matrix elements in quantum mechanics, focusing on the classical limit and different picture representations.

Stochastic Models for Communications

Covers stochastic models for communication systems, including concepts like stochastic processes and Markov chains.

Random Permutations and Probabilities

Explores random permutations and probabilities in various scenarios.

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