Sandia Uncertainty Quantification Toolkit

Material for a Bayesian statistics workshop

High-performance library for approximate inference on discrete Bayesian networks on GPU and CPU

Markov Chain Monte Carlo MCMC methods are implemented in various languages (including R, Python, Julia, Matlab)

Repository for example Hierarchical Drift Diffusion Model (HDDM) code using JAGS in Python. These scripts provide useful examples for using JAGS with pyjags, the JAGS Wiener module, mixture modeling in JAGS, and Bayesian diagnostics in Python.

Repository for example Hierarchical Drift Diffusion Model (HDDM) code using JAGS in R. These scripts provide useful examples for using JAGS with R2jags, the JAGS Wiener module, mixture modeling in JAGS, and Bayesian diagnostics in R.

R library for the work by Thraves, C. Ubilla, P. and Hermosilla, D.: 'Fast Ecological Inference Algorithm for the R×C Case'

Basic building blocks in Bayesian statistics.

This is a repository for the ParaMonte library examples. For more information, visit:

Differentiable Probabilistic Models

Material for a workshop on NIMBLE

DiffeRential Evolution Adaptive Metropolis algorithm: MATLAB and Python Toolbox

This repository contains code, data, output, and figures associated with the A univariate extreme value analysis and change point detection of monthly discharge in Kali Kupang, Central Java, Indonesia manuscript

Modelled COVID-19 pandemic with a system of 9 first order differential equations. The system was fitted to the values of the pandemic in Italy, UK, India, Brazil and Sweden, and numerically solved using MCMC statistical methods in python’s lmfit module. Estimates of the real number of infected people and predictions for the future were then made.

Final year undergraduate project focusing on inverse problems and Markov chain Monte Carlo methods.

Exploratory project on Metropolis sampling and MCMC methods.

Use this to determine the optimal route to go on a search for shortage struck essential commodities (gasoline, water, toilet paper etc.) using information from social media

A collection of MCMC methods in Python using Numpy and Scipy

This module is an efficient and flexible implementation of various Sequential Monte Carlo (SMC) methods. Bayesian updates occur for both latent states and model parameters using joint inference.

This repository provides a package that allows the implementation of Conditional Particle Filter easily. Conditional Particle Filter can be viewed as an MCMC method with invariant distribution as the smoothing distribution of a partially observed diffusion model.