A Bayesian Framework for Persistent Homology

DOI10.1137/19M1268719zbMath1484.62032arXiv1901.02034OpenAlexW3005419229MaRDI QIDQ5027017

Vasileios Maroulas, Farzana Nasrin, Christopher Oballe

Publication date: 3 February 2022

Published in: SIAM Journal on Mathematics of Data Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1901.02034

zbMATH Keywords

topological data analysisintensitymarked Poisson point processesatom probe tomographyBayesian inference and classificationhigh entropy alloys

Mathematics Subject Classification ID

Bayesian inference (62F15) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55) Topological data analysis (62R40)

Related Items

Nonparametric Estimation of Probability Density Functions of Random Persistence Diagrams ⋮ Bayesian topological signal processing ⋮ Topological data analysis approaches to uncovering the timing of ring structure onset in filamentous networks ⋮ ADLGM: an efficient adaptive sampling deep learning Galerkin method ⋮ Bayesian topological learning for classifying the structure of biological networks ⋮ Some recent trends in embeddings of time series and dynamic networks ⋮ Topological learning for brain networks ⋮ The (homological) persistence of gerrymandering ⋮ ToFU: topology functional units for deep learning ⋮ A random persistence diagram generator

Uses Software

• Spglib
• AFLOW-SYM
• PersistenceImages

Cites Work

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