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Bayesian computation meets topology.
Trans. Machine Learn. Res. 2024, accepted (2024)
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.
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Publication type
Article: Journal article
Document type
Scientific Article
ISSN (print) / ISBN
2835-8856
e-ISSN
2835-8856
Quellenangaben
Volume: 2024
Publisher
Journal of Machine Learning Research Inc.
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of AI for Health (AIH)