PyVBMC: Efficient Bayesian inference in Python

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dc.contributor.affiliationUniversity of Helsinki - Huggins, Bobby
dc.contributor.affiliationUniversity of Helsinki - Li, Chengkun
dc.contributor.affiliationUniversity of Helsinki - Tobaben, Marlon
dc.contributor.affiliationUniversity of Helsinki - Aarnos, Mikko J.
dc.contributor.affiliationUniversity of Helsinki - Acerbi, Luigi
dc.contributor.authorHuggins, Bobby
dc.contributor.authorLi, Chengkun
dc.contributor.authorTobaben, Marlon
dc.contributor.authorAarnos, Mikko J.
dc.contributor.authorAcerbi, Luigi
dc.date.accessioned2025-03-24T15:11:48Z
dc.date.issued2023-05-24
dc.date.issued2023-05-24
dc.descriptionThis upload archives the v1.0.1 release of PyVBMC, as prepared for submission to the Journal of Open Source Software. PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference for *black-box* computational models. VBMC is an approximate inference method designed for efficient parameter estimation and model assessment when model evaluations are mildly-to-very expensive (e.g., a second or more) and/or noisy.
dc.identifierhttps://doi.org/10.5281/zenodo.7966315
dc.identifier.urihttps://hydatakatalogi-test-24.it.helsinki.fi/handle/123456789/9368
dc.rightsOpen
dc.rights.licensebsd-3-clause
dc.subjectBayesian inference
dc.subjectmachine learning
dc.subjectprobabilistic modeling
dc.subjectcomputational statistics
dc.titlePyVBMC: Efficient Bayesian inference in Python
dc.typesoftware
dc.typesoftware

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