This lecture covers information measures such as entropy and Kullback-Leibler divergence, along with data processing inequality. It explains probability kernels, equality conditions, and proofs related to these concepts.
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Covers information measures like entropy, Kullback-Leibler divergence, and data processing inequality, along with probability kernels and mutual information.
Explores the concept of entropy expressed in bits and its relation to probability distributions, focusing on information gain and loss in various scenarios.
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