Maximal Correlation: Information Measures

Related lectures (32)

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On entropy and mutual information explores quantifying information in data science through probability distributions.

Explores mutual information in biological data, emphasizing its role in quantifying statistical dependence and analyzing protein sequences.

Covers the basics of information theory and its applications in various fields.

Covers variational representation and information measures such as entropy and mutual information.

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

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

Covers the basics of information theory, focusing on Shannon's setting and channel transmission.

Explores mutual information for quantifying statistical dependence between variables and inferring probability distributions from data.

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

Covers information measures like entropy and Kullback-Leibler divergence.