Bambi (software)

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Bambi
Original author(s)Bambinos
Initial releaseMay 15, 2016 (2016-05-15)
Repositorygithub.com/bambinos/bambi
Written inPython
Operating systemUnix-like, macOS, Windows
PlatformIntel x86 – 32-bit, x64
TypeStatistical package
License MIT License
Websitebambinos.github.io/bambi/

Bambi is a high-level Bayesian model-building interface written in Python. It works with the PyMC probabilistic programming framework. Bambi provides an interface to build and solve Bayesian generalized (non-)linear multivariate multilevel models.[1][2][3][4][5][6][7][8][9][10]

Bambi is an open source project, developed by the community and is an affiliated project of NumFOCUS.

Etymology

Bambi is an acronym for BAyesian Model-Building Interface.

Library features

  • Model specification using a Wilkison-like formula style
  • Bayesian inference using MCMC and Variational Inference methods
  • Interface with ArviZ, as Bambi returns an InferenceData object
  • Model interpretation via conditional adjusted comparisons, predictions, and slopes
  • A wide array of response families
  • Default priors that the users can modify if needed

See also

  • Stan, a probabilistic programming language for statistical inference written in C++

References

  1. ^ Mikkola, Petrus; Martin, Osvaldo A.; Chandramouli, Suyog; Hartmann, Marcelo; Abril Pla, Oriol; Thomas, Owen; Pesonen, Henri; Corander, Jukka; Vehtari, Aki; Kaski, Samuel; Bürkner, Paul-Christian; Klami, Arto (2023). "Prior Knowledge Elicitation: The Past, Present, and Future". Bayesian Analysis. International Society for Bayesian Analysis: 1–33. arXiv:2112.01380. doi:10.1214/23-BA1381.
  2. ^ Štrumbelj, Erik; Bouchard-Côté, Alexandre; Corander, Jukka; Gelman, Andrew; Rue, Håvard; Murray, Lawrence; Pesonen, Henri; Plummer, Martyn; Vehtari, Aki (2024). "Past, Present and Future of Software for Bayesian Inference". Statistical Science. 39 (1). Institute of Mathematical Statistics: 46–61. doi:10.1214/23-STS907. hdl:10754/694575.
  3. ^ Martin, OA; Kumar, R; Lao, J (2021). Bayesian Modeling and Computation in Python. Taylor & Francis.
  4. ^ Qasim, SE; Mohan, UR; Stein, JM; Jacobs, J (2023). "Neuronal activity in the human amygdala and hippocampus enhances emotional memory encoding". Nature Human Behaviour. 7 (5): 754–764. doi:10.1038/s41562-022-01502-8. PMID 36646837.
  5. ^ Pettine, WW; Raman, DV; Redish, AD (2023). "Human generalization of internal representations through prototype learning with goal-directed attention". Nature Human Behaviour. 7 (3): 442–463. doi:10.1038/s41562-023-01543-7. PMID 36894642.
  6. ^ Pudhiyidath, A; Morton, NW; Viveros Duran, R; Schapiro, AC; Momennejad, I; Hinojosa-Rowland, DM; Molitor, RJ; Preston, AR (2022). "Representations of Temporal Community Structure in Hippocampus and Precuneus Predict Inductive Reasoning Decisions". Journal of Cognitive Neuroscience. 34 (10): 1736–1760. doi:10.1162/jocn_a_01864. PMC 10262802. PMID 35579986.
  7. ^ Michiels, Lien; Vannieuwenhuyze, Jorre; Leysen, Jens; Verachtert, Robin; Smets, Annelien; Goethals, Bart (2023). "How Should We Measure Filter Bubbles? A Regression Model and Evidence for Online News". Proceedings of the 17th ACM Conference on Recommender Systems. RecSys '23. Association for Computing Machinery. pp. 640–651. doi:10.1145/3604915.3608805. ISBN 979-8-4007-0241-9.
  8. ^ Kallioinen, N; Paananen, T; Bürkner, PC (2024). "Detecting and diagnosing prior and likelihood sensitivity with power-scaling". Statistics and Computing. 34 (1): 57. doi:10.1007/s11222-023-10366-5.
  9. ^ Gehmacher, Q; Schubert, J; Schmidt, F (2024). "Eye movements track prioritized auditory features in selective attention to natural speech". Nature Communications. 15: 3692. Bibcode:2024NatCo..15.3692G. doi:10.1038/s41467-024-48126-2. PMC 11063150.
  10. ^ Abril-Pla, O; Andreani, V; Carroll, C; Dong, L; Fonnesbeck, CJ; Kochurov, M; Kumar, R; Lao, J; Luhmann, CC; Martin, OA; Osthege, M; Vieira, R; Wiecki, T; Zinkov, R (2023). "PyMC: a modern, and comprehensive probabilistic programming framework in Python". PeerJ Computer Science. 9: e1516. doi:10.7717/peerj-cs.1516. PMC 10495961. PMID 37705656.
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