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