https://github.com/isms/mcmc-rs

A Rust library implementing various MCMC diagnostics and utilities, such as Gelman Rubin potential scale reduction factor (R hat), effective sample size, chain splitting, and others.
https://github.com/isms/mcmc-rs

mcmc mcmc-diagnostics probabilistic-programming statistics

Last synced: about 1 month ago
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A Rust library implementing various MCMC diagnostics and utilities, such as Gelman Rubin potential scale reduction factor (R hat), effective sample size, chain splitting, and others.

• Host: GitHub
• URL: https://github.com/isms/mcmc-rs
• Owner: isms
• License: mit
• Created: 2020-09-08T14:21:48.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2021-02-10T19:59:18.000Z (almost 4 years ago)
• Last Synced: 2024-12-02T01:09:23.452Z (about 2 months ago)
• Topics: mcmc, mcmc-diagnostics, probabilistic-programming, statistics
• Language: Rust
• Homepage: https://docs.rs/mcmc
• Size: 360 KB
• Stars: 5
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
MCMC diagnostics in Rust

====

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A Rust library implementing various MCMC diagnostics and utilities, such as Gelman Rubin

potential scale reduction factor (R hat), effective sample size (ESS), chain splitting,

and others.

This crate is language agnostic and intended to work with the outputs of any MCMC sampler

(e.g. Stan, PyMC3, Turing.jl, etc).

Implementation

--------------

Currently we expect plain vectors of `f64` floating point numbers, but this may be

worth generalizing to `f32`s as well (see roadmap below).

Implementations for some of these diagnostics vary slightly, so reference implementations

are based on [Stan](https://github.com/stan-dev/stan), and unit tests are adapted from the

Stan codebase to ensure matching behavior.

Roadmap

-------

**Diagnostics**

- [X] Potential scale reduction factor

- [X] Split potential scale reduction factor

- [X] Effective sample size

- [X] Monte Carlo Standard Error

**Utilities**

- [X] Split chains as recommended in Vehtari, et al 2019

- [ ] Thinning

**Data structures**

- [ ] Introduce `Num` type to generalize our implementations to work for `f32` or `f64`.

- [ ] Would it be helpful to have some kind of struct that can represent

      one or more sample chains with a parameter name?

**Performance**

- [ ] Remove unnecessary copying or allocation

References

----------

  [1]: Stephen P. Brooks and Andrew Gelman. General Methods for Monitoring

       Convergence of Iterative Simulations.

       _Journal of Computational and Graphical Statistics_, 7(4), 1998.

  [2]: Andrew Gelman and Donald B. Rubin. Inference from Iterative Simulation

       Using Multiple Sequences. _Statistical Science_, 7(4):457-472, 1992.

  [3]: Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, Paul-Christian

       Burkner. Rank-normalization, folding, and localization: An improved R-hat

       for assessing convergence of MCMC, 2019. Retrieved from

       [http://arxiv.org/abs/1903.08008]().

  [4]: Geyer, Charles J. Introduction to Markov Chain Monte Carlo.

       _Handbook of Markov Chain Monte Carlo_, edited by Steve Brooks, Andrew Gelman,

       Galin L. Jones, and Xiao-Li Meng. Chapman; Hall/CRC. 2011.

Acknowledgements

----------------

_Thanks to [Ivan Ukhov](https://github.com/IvanUkhov) for generously providing

the `mcmc` namespace on Cargo._