⏪ R package for: Reconstructing Etiology with Binary Decomposition

Efficient MCMC Algorithm for Fitting the Semi-Markov Stochastic SIR Model to Incidence Counts.

Simulation of markov chain.

Markov Chain Monte Carlo Simulation for COVID-19 Incidence.

Radial neighbors GP

Bayesian analysis of children's height data from Galton's experiment. Finished 2022

Simulation of COVID-19 cases arising from high-risk contacts

Applied analysis on the Bayesian student-t "Robust" regression model with Jeffrey's prior. Compared its model performance and robustness of posterior distributions with the Gaussian model when outliers are present.

A stokhazesthai (stochastic) process, also called a random process, is one in which outcomes are uncertain (MAT 455, ISU).

Homework done in R

Statistical models and proofs done for my Bayesian Statistics and Markov Chain/Monte Carlo class at Yale. Original problem statements and prompts available upon request!

A lightweight R-language implementation of the affine-invariant sampling method of Goodman & Weare (2010)

Applies Bayesian techniques for analysing various factors that can influence a UK university's graduate prospects rating from the HESA SFR247 and Complete University Guide table.

Metropolis and Nested Sampling in R

Stochastic Approximation Cut Algorithm for Inference in Modularised Bayesian Models

This repository contains code for the paper `Sequential Monte Carlo algorithms for agent-based models of disease transmission' by Nianqiao (Phyllis) Ju, Jeremy Heng and Pierre Jacob.

Reconstruction of Transmission Chains from Surveillance Data

Datasets and code for CS226 (Machine Learning) Research Project (December 2016). The endproduct is a reversible jump Markov Chain Monte Carlo algorithm to define the appropriate clusters of genetic ancestry with a sample of human genomes.

A friendly MCMC framework

Pre-compiled CmdStan models in R packages