Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real-world datasets.

Additional univariate and multivariate distributions

Several examples of multivariate techniques implemented in R, Python, and SAS. Multivariate concrete dataset retrieved from https://archive.ics.uci.edu/ml/datasets/Concrete+Slump+Test. Credit to Professor I-Cheng Yeh.

Copula fitting in Python.

An object-oriented approach to implement anomaly detection in Python using semi-supervised learning

Multivariate independent comparison of observations

道家：算卜、占卜、分析彩券

Multivariate independent comparison of observations.

Robust estimation using heavy-tailed distributions

A Python Package to Create Synthetic Tabular Data

Random data generator from scratch. (Using numpy for simple mathematical functions only)

Collection of missing multivariate random number distributions for Modern C++ with STL-like API.

Machine Learning algorithms using Sklearn and Scikit python libraries for comparing their behaviour on anomaly detection

A simple utility to perform sampling from multivariate distributions (supported by a PyTorch backend)

Random Simplex - https://doi.org/10.5281/zenodo.12808899

Programa Computarizado, elaborado en el lenguaje de programacion MATLAB, referente al Trabajo Especial de Grado para optar al Título de Ingeniero Industrial, en la Facultad de Ingeniería de la Universidad de Carabobo, año 2015; la cual fue reconocida con mención honorífica.

Implemented from scratch a sampling method to draw samples from a multivariate Normal (MVN) distribution in JAX.