PDSampler.jl is a package designed to provide an efficient, flexible, and expandable framework for samplers based on Piecewise Deterministic Markov Processes and their applications. This includes the Bouncy Particle Sampler and the Zig-Zag Sampler. See the references at the bottom of this page.
The project is hosted by the Alan Turing Institute (ATI). If you encounter problems, please open an issue on Github. If you have comments or wish to collaborate, please open an issue on Github.
Using the Package
To install the package, use the following command inside the Julia REPL:
To load the package, use the command:
You can also run the tests with
Pkg.test("PDSampler") and update to the latest Github version with
The following examples will introduce you to the functionalities of the package.
These pages introduce you to the core of the package and its interface. This is useful if you are looking into expanding the code yourself to add a capacity or a specific model.
Alexandre Bouchard-Côté, Sebastian J. Vollmer and Arnaud Doucet, The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method, arXiv preprint, 2015.
Joris Bierkens, Alexandre Bouchard-Côté, Arnaud Doucet, Andrew B. Duncan, Paul Fearnhead, Gareth Roberts and Sebastian J. Vollmer, Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains, arXiv preprint, 2017.
Joris Bierkens, Paul Fearnhead and Gareth Roberts, The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data, arXiv preprint, 2016.
Changye Wu, Christian Robert, Generalized Bouncy Particle Sampler, arXiv preprint, 2017.