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.

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

Multivariate independent comparison of observations

Lottery analysis

Copula fitting in Python.

Robust estimation using heavy-tailed distributions

Multivariate independent comparison of observations.

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.

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

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.

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

A Collection of Basic Utilities for Statistical Analyses