This repository contains the source code for a Shiny app designed to provide an intuitive and visual understanding of the Metropolis-Hastings algorithm and several Markov Chain Monte Carlo (MCMC) diagnostic methods.
The project has two main aims:
- To understand the Metropolis-Hastings algorithm and several MCMC diagnostic methods at a more intuitive and visual level through animated and interactive plots.
- To present the first aim in a cohesive and compact manner to those unfamiliar with MCMC and the R-Programming Language. Why MCMC
MCMC methods are a class of algorithms for sampling from a probability distribution. By constructing a Markov chain that has the desired distribution as its equilibrium distribution, one can obtain a sample of the desired distribution by recording states from the chain. The more steps that are included, the more closely the distribution of the sample matches the actual desired distribution.
R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. It is widely used among statisticians and data miners for developing statistical software and data analysis.
The app provides a visual and interactive way to understand the time-dependent nature of a Markov chain and the amount of visually appealing parameters optimal for creating animated illustrations.
You can access the live demo of the app here.
Contributions are welcome. Please open an issue to discuss your ideas or submit a pull request with your changes.