Sandia Uncertainty Quantification Toolkit
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Updated
Apr 9, 2024 - Fortran
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:
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
Final year undergraduate project focusing on inverse problems and Markov chain Monte Carlo methods.
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
Code implementations of the methods discussed in Generalized Fiducial Inference on Differentiable Manifolds by A. Murph, J. Hannig, and J. Williams.
Using the Ising Model and a Monte-Carlo Markov Chain Approach to Illustrate Magnetic Phase Transition
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.
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.
Adaptive paralelle tempering for sampling multi-modal posteriors in NIMBLE.
Running Monte Carlo - Markov Chain algorithm on synthesized spectral models made by CLOUDY to compare them with data from CECILIA survey
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