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项目作者： isms

项目描述 ：
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.
高级语言： Rust
项目地址: git://github.com/isms/mcmc-rs.git
创建时间： 2020-09-08T14:21:48Z
项目社区：https://github.com/isms/mcmc-rs
开源协议：MIT License
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MCMC diagnostics in Rust

Crates.io
docs.rs
MIT licensed
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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 f32s as well (see roadmap below).

Implementations for some of these diagnostics vary slightly, so reference implementations
are based on 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
    1. 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 for generously providing
the mcmc namespace on Cargo.