Bayesian Computation Meets Topology
DOKPE
- Rohrscheidt, Julius von Technical University of Munich, Germany
- Rieck, Bastian ORCID University of Fribourg
- Schmon, Sebastian M. ORCID Altos Labs, Cambridge, UK
- 2024
Published in:
- Transactions on Machine Learning Research. - OpenReview. - 2024
bayesian methods
computational topology
persistent homology
topological data analysis
approximate bayesian computation
parameter estimation
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.
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
- License
- CC BY
- Open access status
- gold
- Identifiers
-
- ISSN 2835-8856
- Persistent URL
- https://folia.unifr.ch/unifr/documents/330293
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