Covers information measures like entropy, Kullback-Leibler divergence, and data processing inequality, along with probability kernels and mutual information.
Covers the basics of information theory, focusing on Shannon's setting and channel transmission.
Covers information measures like entropy, joint entropy, and mutual information in information theory and data processing.
Explores mutual information for quantifying statistical dependence between variables and inferring probability distributions from data.
Discusses mutual information, data processing inequality, and properties related to leakage in discrete systems.
Explores tail bounds, information bounds, and maximal leakage in the context of generalization error.
Delves into quantifying entropy in neuroscience data, exploring how neuron activity represents sensory information and the implications of binary digit sequences.
Covers information measures, tail bounds, subgaussions, subpossion, independence proof, and conditional expectation.
Explores entropy, KL divergence, and maximum entropy principle in probability models for data science.
