ArviZ

ArviZ (/ˈɑːrvɪz/ AR-vees) is a Python package for exploratory analysis of Bayesian models [1][2] it offers data structures for manipulating data that it is common in Bayesian analysis, like numerical samples from the posterior, prior predictive and posterior predictive distributions as well as observed data. Additionally, many numerical/visual diagnostics and plots are available. The ArviZ name is derived from reading "rvs" (the short form of random variates) as a word instead of spelling it and also using the particle "viz" usually used to abbreviate visualization.

ArviZ
Original author(s)ArviZ Development Team
Initial releaseJuly 21, 2018 (2018-07-21)
Stable release
0.9.0 / June 23, 2020 (2020-06-23)
Written inPython
Operating systemUnix-like, Mac OS X, Microsoft Windows
PlatformIntel x86 – 32-bit, x64
TypeStatistical package
License Apache License, Version 2.0
Websitearviz-devs.github.io/arviz/

ArviZ is an open source project, developed by the community and is an affiliated project of NumFocus.[3] and it has been used to help interpret inference problems in several scientific domains, including astronomy[4], neuroscience [5], physics [6] and statistics [7] [8].


Library features
  • InferenceData object for Bayesian data manipulation. This object is based on xarray
  • Plots using two alternative backends matplotlib or bokeh
  • Numerical summaries and diagnostics for MCMC methods.
  • Integration with established probabilistic programming languages including; PyStan (the Python interface of Stan), PyMC [9], Edward [10] Pyro [11], and easily integrated with novel or bespoke Bayesian analyses. ArviZ is also available in Julia, using the ArviZ.jl interface

See also

bayesplot is an R package providing an extensive library of plotting functions for use after fitting Bayesian models (typically with MCMC)

loo R package for efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models

References
  1. Ravin Kumar, Colin Carroll, Ari Hartikainen, and Osvaldo Martin (2019). ArviZ a unified library for exploratory analysis of Bayesian models in Python. JOSS 4(33), 1143, https://doi.org/10.21105/joss.0114
  2. Martin, Osvaldo (2018). Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ. Packt Publishing Ltd. ISBN 9781789341652.
  3. "NumFOCUS Affiliated Projects". NumFOCUS | Open Code = Better Science. Retrieved 2019-30-11. Check date values in: |access-date= (help)
  4. Will M. Farr, Maya Fishbach, Jiani Ye, and Daniel E. Holz (2019) A Future Percent-level Measurement of the Hubble Expansion at Redshift 0.8 with Advanced LIGO The American Astronomical Society https://doi.org/10.3847%2F2041-8213%2Fab4284
  5. Busch-Moreno, Simon and Tuomainen, Jyrki and Vinson, David (2020) Semantic and Prosodic Threat Processing in Trait Anxiety: Is Repetitive Thinking Influencing Responses? https://doi.org/10.1101/2020.01.24.918375
  6. Petar Jovanovski1 and Ljupco Kocarev (2019) Bayesian consensus clustering in multiplex networks Chaos 29, 103142 https://doi.org/10.1063/1.5120503
  7. Guangyao Zhou (2019) Exploring Discrete Analogs of Hamiltonian Monte Carlo arXiv https://arxiv.org/abs/1909.04852
  8. Matthew M. Graham (2019) Manifold Markov chain Monte Carlo methods for Bayesian inference in a wide class of diffusion models arXiv https://arxiv.org/abs/1912.02982
  9. Salvatier J, Wiecki TV, Fonnesbeck C. (2016) Probabilistic programming in Python using PyMC3. PeerJ Computer Science 2:e55 https://doi.org/10.7717/peerj-cs.55
  10. Tran, D., Kucukelbir, A., Dieng, A. B., Rudolph, M., Liang, D., & Blei, D. M. (2016) Edward: A library for probabilistic modeling, inference, and criticism. arXiv https://arxiv.org/abs/1610.09787
  11. Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., et al (2018) Pyro: Deep Universal Probabilistic Programming arXiv https://arxiv.org/abs/1810.09538
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