Information Theory: Quantifying Messages and Source Entropy

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Information Theory Basics

Introduces information theory basics, including entropy, independence, and binary entropy function.

Random Walks and Moran Model in Population Genetics

Explores random walks, Moran model, bacterial chemotaxis, entropy, information theory, and coevolving sites in proteins.

Conditional Entropy and Data Compression Techniques

Discusses conditional entropy and its role in data compression techniques.

Information Measures

Covers information measures like entropy, Kullback-Leibler divergence, and data processing inequality, along with probability kernels and mutual information.

Quantifying Information: Probability, Entropy, and Constraints

Explores quantifying information based on probability, entropy, and constraints in communication systems.

Achievable Rate & Capacity

Explores achievable rate, channel capacity, spectral efficiency, and fading channels in wireless communication systems.

Information Measures

Covers information measures like entropy and Kullback-Leibler divergence.

Random Variables and Information Theory Concepts

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

Source Coding Theorems: Entropy and Source Models

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

Entropy Bounds: Conditional Entropy Theorems

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