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.

Basic building blocks in Bayesian statistics.

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

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

Differentiable Probabilistic Models

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.

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

A collection of MCMC methods in Python using Numpy and Scipy

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.

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

Implementation of a parameter estimation method without bias, applied to the PUMP problem

Ising Model Simulation & lab report with applications to epidemiology. COVID-19 is a case study.

732A64 Master Project

Here, I tried to learn some Markov chain Monte Carlo methods.