Mutual Information in Biological Data

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Related lectures (31)

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

Quantifying Statistical Dependence

Delves into quantifying statistical dependence through covariance, correlation, and mutual information.

Quantifying Statistical Dependence: Covariance and Correlation

Explores covariance, correlation, and mutual information in quantifying statistical dependence between random variables.

Variational Formulation: Information Measures

Explores variational formulation for measuring information content and divergence between probability distributions.

Information Measures

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

Information Theory: Review and Mutual Information

Reviews information measures like entropy and introduces mutual information as a measure of information between random variables.

Mutual Information: Understanding Random Variables

Explores mutual information, quantifying relationships between random variables and measuring information gain and statistical dependence.

Dependence and Correlation

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

Elements of Statistics: Probability and Random Variables

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Central Limit Theorem: Properties and Applications

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.