Journal article

Bayesian Computation Meets Topology

DOKPE

  • 2024
Published in:
  • Transactions on Machine Learning Research. - OpenReview. - 2024
English Computational topology recently started to emerge as a novel paradigm for characterising
the ‘shape’ of high-dimensional data, leading to powerful algorithms in (un)supervised representation learning. While capable of capturing prominent features at multiple scales, topological methods cannot readily be used for Bayesian inference. We develop a novel
approach that bridges this gap, making it possible to perform parameter estimation in a
Bayesian framework, using topology-based loss functions. Our method affords easy integration into topological machine learning algorithms. We demonstrate its efficacy for parameter estimation in different simulation settings.
Faculty
Faculté des sciences et de médecine
Department
Département d'Informatique
Language
  • English
Other electronic version

Published on

License
CC BY
Open access status
gold
Identifiers
  • ISSN 2835-8856
Persistent URL
https://folia.unifr.ch/unifr/documents/330293
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