CmdStanR: the R interface to CmdStan
-
Updated
Jun 3, 2024 - R
CmdStanR: the R interface to CmdStan
R package for statistical inference using partially observed Markov processes
Pre-compiled CmdStan models in R packages
A friendly MCMC framework
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.
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
Stochastic Approximation Cut Algorithm for Inference in Modularised Bayesian Models
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.
A lightweight R-language implementation of the affine-invariant sampling method of Goodman & Weare (2010)
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 stokhazesthai (stochastic) process, also called a random process, is one in which outcomes are uncertain (MAT 455, ISU).
⏪ R package for: Reconstructing Etiology with Binary Decomposition
Markov Chain Monte Carlo Simulation for COVID-19 Incidence.
Radial neighbors GP
Efficient MCMC Algorithm for Fitting the Semi-Markov Stochastic SIR Model to Incidence Counts.
Bayesian analysis of children's height data from Galton's experiment. Finished 2022
Add a description, image, and links to the markov-chain-monte-carlo topic page so that developers can more easily learn about it.
To associate your repository with the markov-chain-monte-carlo topic, visit your repo's landing page and select "manage topics."