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Density-estimation

This exercise is divided in 3 parts:

  1. Probability density estimation (PDF) of univariate data (kernel.csv) using Kernel (non-parametric) methods. I fit the Bandwidth hyperparameter of the Epanechnikov kernel using the mean integrated square error (MISE).

  2. Gaussian Mixture Models hyperparameter tunning (covariance type and number of components) of multivariate data (diabetes.csv). I use K-fold Cross-Validation approach to calculate the maximum log-likelihood and therefore select the best model.

  3. Same case as in point 1, this time I use K-fold Cross-Validation approach to tune the Bandwidth hyperparameter of the Epanechnikov kernel.