⏾ Concept Map

ArviZ ○›｜Definition｜1st｜20260814145542-00-⌔

ArviZ - Wikipedia

ArviZ

📊 ➺ 🖼️ ➺

ArviZ (/ˈɑːrvɪz/AR-vees) is a Python package for exploratory analysis of Bayesian models.1234 It is specifically designed to work with the output of probabilistic programming libraries like PyMC, Stan, and others by providing a set of tools for summarizing and visualizing the results of Bayesian inference in a convenient and informative way. ArviZ also provides a common data structure for manipulating and storing data commonly arising in Bayesian analysis, like posterior samples or observed data.

ArviZ is an open source project, developed by the community and is an affiliated project of NumFOCUS.5 and it has been used to help interpret inference problems in several scientific domains, including astronomy,6 neuroscience,7 physics8 and statistics.910

Printed 2026-08-14.

(echo:: @ ᯤ)

Footnotes

  1. Martin, Osvaldo A.; Abril-Pla, Oriol; Deklerk, Jordan; Axen, Seth D.; Carroll, Colin; Hartikainen, Ari; Vehtari, Aki (2026). “ArviZ: a modular and flexible library for exploratory analysis of Bayesian models”. Journal of Open Source Software. 11 (119): 9889. doi:10.21105/joss.09889. ↩

  2. Martin, Osvaldo (2024). Bayesian Analysis with Python - Third Edition: A practical guide to probabilistic modeling. Packt Publishing Ltd. ISBN 9781805127161. ↩

  3. Martin, Osvaldo; Kumar, Ravin; Lao, Junpeng (2021). Bayesian Modeling and Computation in Python. CRC-press. pp. 1–420. ISBN 9780367894368. Retrieved 7 July 2022. ↩

  4. Gelman, Andrew; Vehtari, Aki; Simpson, Daniel; Margossian, Charles; Carpenter, Bob; Yao, Yuling; Kennedy, Lauren; Gabry, Jonah; Bürkner, Paul-Christian; Martin, Modrák (2021). “Bayesian Workflow”. arXiv:2011.01808 [stat.ME]. ↩

  5. “NumFOCUS Affiliated Projects”. NumFOCUS | Open Code = Better Science. Retrieved 2019-11-30. ↩

  6. Farr, Will M.; Fishbach, Maya; Ye, Jiani; Holz, Daniel E. (2019). “A Future Percent-level Measurement of the Hubble Expansion at Redshift 0.8 with Advanced LIGO”. The Astrophysical Journal. 883 (2): L42. arXiv:1908.09084. Bibcode:2019ApJ…883L..42F. doi:10.3847/2041-8213/ab4284. S2CID 202150341. ↩

  7. Busch-Moreno, Simon; Tuomainen, Jyrki; Vinson, David (2021). “Trait anxiety effects on late phase threatening speech processing: Evidence from electroencephalography”. European Journal of Neuroscience. 54 (9): 7152–7175. doi:10.1111/ejn.15470. PMID 34553432. ↩

  8. Jovanovski, Petar; Kocarev, Ljupco (2019). “Bayesian consensus clustering in multiplex networks”. Chaos: An Interdisciplinary Journal of Nonlinear Science. 29 (10): 103142. Bibcode:2019Chaos..29j3142J. doi:10.1063/1.5120503. PMID 31675792. S2CID 207834500. ↩

  9. Zhou, Guangyao (2019). “Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables”. arXiv:1909.04852 [stat.CO]. ↩

  10. Graham, Matthew M.; Thiery, Alexandre H.; Beskos, Alexandros (2019). “Manifold Markov chain Monte Carlo methods for Bayesian inference in a wide class of diffusion models”. arXiv:1912.02982 [stat.CO]. ↩