BayesianSampler is a simple, extensible module for understanding Bayesian Network, Joint Probability and Sampling process. It built on top of Numpy and Pandas to provide an intuitive and working numbers so student can learn better about probabilistic model.

Probabilistic programming in Python built on Google Jax

Pytorch implementation of Variational Autoencoders - a popular deep generative probabilistic graphical model.

Asynchronous message passing for probabilistic graphical models

Reasoner for UCO Ontology

Profile Stan workloads

Build probabilistic model for AND dataset using pgmpy

Multi-class classification using Naive Bayes on "Twitter US Airline Sentiment" dataset.

Probabilistic Graphical Models for Stereo Disparity Map Reconstruction by Factor Graph and Belief Propagation Maximum A Posteriori

Probabilistic graphical models to learn Origin-Destination matrices in transportation networks using TensorFlow

Undirected graphical models are compact representations of joint probability distributions over random variables. To solve inference tasks of interest, graphical models of arbitrary topology can be trained using empirical risk minimization. However, to solve inference tasks that were not seen during training, these models (EGMs) often need to be…

Repository dedicated to the well-known statistics topic.

Finding of the missed values in the adjacency matrix of a big undirected weighted graph by utilizing probabilistic graphical models. The adjacency matrix's values were modeled with Poisson distribution and Gamma prior.

Detailed Analysis of Probability and stochastic process with reference "Multiscale approach predictions for biological outcomes in ion-beam cancer therapy"

NLP Models

Python implementation for DNA trace reconstruction with marker code, MIT licensed

Pyro implementation of PLSM

This is a collection of algorithms and models written in Python for probabilistic programming. The main focus of the package is on Bayesian reasoning by using Bayesian networks, Markov networks, and their mixing.