Collection of probabilistic models and inference algorithms

Build dirichletprocess objects for data analysis

Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning Workshop 2019)

Brief introduction and implementations of related concepts to Dirichlet Processes: GEM distribution, Polya Urn, Chinese restaurant process, Stick-Breaking construction, and Posterior of a DP.

ACM CHIL 2020: "Survival Cluster Analysis"

An R Package for Bayesian Nonparametric Clustering. We plan to implement several models.

This code belongs to ACL conference paper entitled as "An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering"

Code for our UAI '20 paper "Scalable and Flexible Clustering of Grouped Data via Parallel and Distributed Sampling in Versatile Hierarchical Dirichlet Processes"

Accurate estimation of conditional categorical probability distributions using Hierarchical Dirichlet Processes

[ECCV'24] cDP-MIL: Robust Multiple Instance Learning via Cascaded Dirichlet Process

R implementation of the Dirichlet Process Gaussian Mixture Model (with MCMC)

Code to work with Dirichlet processes and hierarchical Dirichlet processes and perform inference using MCMC.

Implementation of Rasmussen's paper on The Infinite Gaussian Mixture Model

Predicting Purchase Rates in Stationary Markets

A demo of Dirichlet Distribution, Dirichlet Process and the Chinese Restaurant Process based GMM Clustering

This repository captures code developed during my PhD at the University of Bath and includes the implementation of the DP-GP-LVM model.

Codes for Chandra, et al. (2021+). Escaping the curse of dimensionality in Bayesian model based clustering. Please refer to the original paper for details https://arxiv.org/abs/2006.02700

Non-parametric Bayesian Model

This project was realized for the Bayesian Statistics course, held at Politecnico di Milano, A.Y. 2022/2023.

Probabilistic Models of Human and Machine Intelligence