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- Title
Variational inference for the latent shrinkage position model.
- Authors
Gwee, Xian Yao; Gormley, Isobel Claire; Fop, Michael
- Abstract
The latent position model (LPM) is a popular method used in network data analysis where nodes are assumed to be positioned in a p$$ p $$‐dimensional latent space. The latent shrinkage position model (LSPM) is an extension of the LPM which automatically determines the number of effective dimensions of the latent space via a Bayesian nonparametric shrinkage prior. However, the LSPM's reliance on Markov chain Monte Carlo for inference, while rigorous, is computationally expensive, making it challenging to scale to networks with large numbers of nodes. We introduce a variational inference approach for the LSPM, aiming to reduce computational demands while retaining the model's ability to intrinsically determine the number of effective latent dimensions. The performance of the variational LSPM is illustrated through simulation studies and its application to real‐world network data. To promote wider adoption and ease of implementation, we also provide open‐source code.
- Subjects
MARKOV chain Monte Carlo; FRUIT drying
- Publication
Stat, 2024, Vol 13, Issue 2, p1
- ISSN
2049-1573
- Publication type
Article
- DOI
10.1002/sta4.685