A random persistence diagram generator

نویسندگان

چکیده

Topological data analysis (TDA) studies the shape patterns of data. Persistent homology is a widely used method in TDA that summarizes homological features at multiple scales and stores them persistence diagrams (PDs). In this paper, we propose random diagram generator (RPDG) generates sequence PDs from ones produced by RPDG underpinned model based on pairwise interacting point processes reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithm. A first example, which synthetic dataset, demonstrates efficacy provides comparison with another for sampling PDs. second example utility to solve materials science problem given real dataset small sample size.

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ژورنال

عنوان ژورنال: Statistics and Computing

سال: 2022

ISSN: ['0960-3174', '1573-1375']

DOI: https://doi.org/10.1007/s11222-022-10141-y