Sandia Uncertainty Quantification Toolkit

Material for a Bayesian statistics workshop

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

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.

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 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'

DiffeRential Evolution Adaptive Metropolis algorithm: MATLAB and Python Toolbox

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

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.

An R package for Bayesian inference on graphical models with mixed data types (continuous, discrete, categorical, and zero-inflated)

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

Exploratory project on Metropolis sampling and MCMC methods.

Adaptive paralelle tempering for sampling multi-modal posteriors in NIMBLE.

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.

Repostory containing a classical simulator of the first version of the Quantum-enhanced MCMC optimization algorithm.