Protein Contact Prediction: Pseudolikelihoods and Potts Models

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Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Gaussian Mixture Models: Likelihood and Covariance Matrix

Explores statistical independence, Gaussian Mixture Models, and fitting data with Gaussian functions.

Quantifying Statistical Dependence

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

Dependence and Correlation

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

Fitting data with one Gauss function

Explains Gaussian functions, modeling data, likelihood function, and maximum likelihood optimization.

Multivariate Statistics: Normal Distribution

Covers the multivariate normal distribution, properties, and sampling methods.

Mutual Information: Continued

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

Mutual Information in Biological Data

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

Maximum Likelihood Estimation: Linear Models

Explores maximum likelihood estimation in linear models, covering Gaussian noise, covariance estimation, and support vector machines for classification problems.

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.