.png){kind=icon}
.png){kind=icon}
Return
Improved Bayesian information criterion for mixture model selection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2015.10.004.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we propose a mixture model selection criterion obtained from the Laplace approximation of marginal likelihood. Our approximation to the marginal likelihood is more accurate than Bayesian information criterion (BIC), especially for small sample size. We show experimentally that our criterion works as good as other well-known criteria like BIC and minimum message length (MML) for large sample size and significantly outperforms them when fewer data points are available. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Model selection
Bayesian method
Minimum message length
Finite mixture models
Laplace approximation
Clustering
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
7.9K
Citations:
1.6W