Bde: A Python Package for Bayesian Deep Ensembles via MILE

MCML Authors

Emanuel Sommer

→ Group David Rügamer
 Statistics, Data Science and Machine Learning

David Rügamer

Prof. Dr.

Core PI

Statistics, Data Science and Machine Learning

Abstract

bde is a user-friendly Python package for Bayesian Deep Ensembles with a particular focus on tabular data. Built on an efficient JAX implementation of the sampling-based inference method Microcanonical Langevin Ensembles (MILE), it provides scikit-learn compatible estimators for fast training, efficient Markov Chain Monte Carlo sampling, and uncertainty quantification in both regression and classification tasks.

misc AAS+26

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Preprint

May. 2026

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Authors

V. Arvanitis • A. Aslanidis • E. Sommer • D. Rügamer

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Links

arXiv GitHub

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Research Area

 A1 | Statistical Foundations & Explainability