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.

Basic building blocks in Bayesian statistics.

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

DiffeRential Evolution Adaptive Metropolis algorithm: MATLAB and Python Toolbox

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.

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

Exploratory project on Metropolis sampling and MCMC methods.

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

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.

A collection of MCMC methods in Python using Numpy and Scipy

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.

Running Monte Carlo - Markov Chain algorithm on synthesized spectral models made by CLOUDY to compare them with data from CECILIA survey

Creating plots illustrating the SED of PKS 1510-089 for the Treball Fin de Master at Universitat Autonoma de Barcelona to complete the master degree in HEP, Astrophysics and Cosmology at IFAE.

Personal Website with Blogposts, Achievements and Ideas

A Bayesian approach to the modeling COVID19 spread based on the Gompertz equation applied to the confirmed cases and fatalities by country.

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

This project implements a decoding algorithm using MCMC (Markov Chain Monte Carlo) methods in R. The approach leverages probabilistic sampling to estimate hidden states in a sequence, commonly used in applications like hidden Markov models and Bayesian inference. The code includes data preprocessing, model setup, and result.

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

R code estimate the FGM copula's dependence parameter using classical and Bayesian methods. Applied to a dengue dataset, it focuses on leukocyte and platelet counts. Classical analysis employs maximum likelihood estimation y moments method, while the Bayesian approach uses Markov Chain Monte Carlo for posterior inference.

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