Information Measures: Entropy and Information Theory

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Quantifying Entropy in Neuroscience Data

Delves into quantifying entropy in neuroscience data, exploring how neuron activity represents sensory information and the implications of binary digit sequences.

Random Variables and Information Theory Concepts

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

Generalization Error

Discusses mutual information, data processing inequality, and properties related to leakage in discrete systems.

Entropy and KL Divergence

Explores entropy, KL divergence, and maximum entropy principle in probability models for data science.

Quantifying Information: Probability, Entropy, and Constraints

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

Entropy Bounds: Conditional Entropy Theorems

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

Information Theory Basics

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

Probability Theory: Joint Marginals and Granger Causality

Covers joint marginals and Granger causality in probability theory, explaining their implications in predicting outcomes.