Bayesian Analysis of Graphical Models
CmdStanR: the R interface to CmdStan
R package for statistical inference using partially observed Markov processes
This repository contains R codes for the manuscript ``Sampling large hyperplane‑truncated multivariate normal distributions''
Pre-compiled CmdStan models in R packages
Bayesian Methods for State Space Models
A friendly MCMC framework
Reconstruction of Transmission Chains from Surveillance Data
Radial neighbors GP
Bayesian analysis of children's height data from Galton's experiment. Finished 2022
A lightweight R-language implementation of the affine-invariant sampling method of Goodman & Weare (2010)
Efficient MCMC Algorithm for Fitting the Semi-Markov Stochastic SIR Model to Incidence Counts.
Simulation of COVID-19 cases arising from high-risk contacts
A stokhazesthai (stochastic) process, also called a random process, is one in which outcomes are uncertain (MAT 455, ISU).
Stochastic Approximation Cut Algorithm for Inference in Modularised Bayesian Models
Markov Chain Monte Carlo Simulation for COVID-19 Incidence.
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.
Metropolis and Nested Sampling in R
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.
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