distribird is an open-source tool that helps data scientists and statisticians generate literature-informed prior distributions for Bayesian model calibration. It provides a web interface for creating and managing priors, streamlining the calibration process in statistical modeling.
In the Data science & ML workbench space, distribird takes a focused approach. It focuses on generating informed prior distributions for Bayesian model calibration using literature data. It is built as an open-source project for data scientists and statisticians. The project is open source (MIT). It ships for the web and the command line, and it can be self-hosted.
HUN-REN AI1Science builds and maintains distribird, and it first shipped in 2026. Development happens publicly on GitHub with 42 commits in the last 90 days. Among its 5 catalogued features are bayesian calibration, prior distribution generation, and literature-informed priors.
Summary written by a language model from the project’s public pages.
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