Probabilistic Interpretation: K-Means & GMM

Related lectures (29)

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Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Explores clustering and dimensionality reduction techniques in finance to clean and simplify data.

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Introduces K-means clustering, the Gaussian mixture model, Jensen's inequality, and the EM algorithm.

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

Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.

Covers the statistical consequences of clustering and the complexities of return level estimation in extreme events.

Explores Bayesian inference for precision in the Gaussian model with known mean, using a Gamma prior and discussing subjective vs objective priors.

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