Split-and-Merge Sampling Algorithm for Hamming-Mixture Models of Categorical Data
Contributo in Atti di convegno
Data di Pubblicazione:
2025
Citazione:
(2025). Split-and-Merge Sampling Algorithm for Hamming-Mixture Models of Categorical Data . Retrieved from https://hdl.handle.net/10446/304869
Abstract:
This work aims to design a Gibbs sampling algorithm for posterior Bayesian inference of a Dirichlet process mixture model based on Hamming distributed kernels, a probability measure built upon the Hamming distance. This model is employed to provide model-based clustering analysis of categorical data with no natural ordering. The proposed algorithm leverages a split-and-merge Markov chain Monte Carlo technique to address the curse of dimensionality issue and improve the search over the space of random partitions.
Tipologia CRIS:
1.4.01 Contributi in atti di convegno - Conference presentations
Elenco autori:
Di Marino, Sara; Galli, Filippo; Argiento, Raffaele; Cremaschi, Andrea; Paci, Lucia
Link alla scheda completa:
Titolo del libro:
Statistics for Innovation III. SIS 2025. Short Papers, Contributed Sessions 2. Italian Statistical Society Series on Advances in Statistics
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