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

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Multivariate independent comparison of observations

A Python Package to Create Synthetic Tabular Data

Multivariate independent comparison of observations.

Robust estimation using heavy-tailed distributions

Jax based probability modelling

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

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

A Collection of Basic Utilities for Statistical Analyses

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

MST.PMDN: 'torch for R' package implementing the deep Multivariate Skew t-Parsimonious Mixture Density Network

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

This repository contains the code for implementing Gaussian Distribution using Python

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