Quantifying Statistical Dependence

Related lectures (30)

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

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

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

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

Covers probability, statistics, independence, covariance, correlation, and random variables.

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

Explores variance, covariance, and practical applications in statistics and probability.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Covers fundamental statistics concepts, including estimation theory, distributions, and the law of large numbers, with practical examples.

Discusses estimation and propagation of uncertainty in random variables and the importance of managing uncertainty in statistical analysis.