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.

Multivariate independent comparison of observations

易经道家：算卜、占卜、秦人牧马、田忌赛马、伯乐识马、分析彩券

Robust estimation using heavy-tailed distributions

A Python Package to Create Synthetic Tabular Data

Gaussian Mixture Model likelihood with support for heterogeneous missing and censored (upper limit) 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.

This repository contains the code for implementing Gaussian Distribution using Python

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

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