Lectures related to Quantifying Statistical Dependence

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.

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Covers z Variable as a Delay Operator, realizable systems, probability theory, stochastic processes, and Hilbert Spaces.

Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.

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

Explores independence and covariance between random variables, discussing their implications and calculation methods.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Explores statistical independence, correlation, Gauss functions, likelihood estimation, and Gaussian Mixture Models for clustering.

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.