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This paper considers the latent Gaussian graphical model, which extends the Gaussian graphical model to handle discrete data as well as mixed data with both continuous and discrete variables by assuming that discrete variables are generated by discretizing latent Gaussian variables. We propose a modified expectationâmaximization (EM) algorithm to estimate parameters in the latent Gaussian model for binary data. We also extend the proposed modified EM algorithm to the latent Gaussian model for mixed data. The conditional dependence structure can be consequently constructed by exploring the sparsity pattern of the precision matrix of the latent variables. We illustrate the performance of our proposed estimator through comprehensive numerical studies and an application to voting data of the United Nations General Assembly.
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Latent bloodstain detection using a selective turn-on NIR fluorescence dye responsive to serum albumin
Latent bloodstains can be visualized using a selective turn-on NIR fluorescence dye responsive to serum albumin. This non-destructive method can detect aged bloodstains and image latent blood fingerprint patterns against colorful backgrounds.
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- PAR ID:
- 10496024
- Publisher / Repository:
- RSC
- Date Published:
- Journal Name:
- RSC Advances
- Volume:
- 13
- Issue:
- 39
- ISSN:
- 2046-2069
- Page Range / eLocation ID:
- 27549 to 27557
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D3RA04320G
