Comparison of two inference approaches in Gaussian graphical models

Date

2017-04-01

Author

Purutçuoğlu Gazi, Vilda
AYYILDIZ DEMİRCİ, EZGİ
Wit, Ernst

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Introduction: The Gaussian Graphical Model (GGM) is one of the well-known probabilistic models which is based on the conditional independency of nodes in the biological system. Here, we compare the estimates of the GGM parameters by the graphical lasso (glasso) method and the threshold gradient descent (TGD) algorithm.

Subject Keywords

Clinical Biochemistry, Biochemistry, Molecular Biology, Biochemistry, medical

URI

https://hdl.handle.net/11511/40762

Journal

TURKISH JOURNAL OF BIOCHEMISTRY-TURK BIYOKIMYA DERGISI

DOI

https://doi.org/10.1515/tjb-2016-0298

Collections

Department of Statistics, Article

Citation Formats

V. Purutçuoğlu Gazi, E. AYYILDIZ DEMİRCİ, and E. Wit, “Comparison of two inference approaches in Gaussian graphical models,” TURKISH JOURNAL OF BIOCHEMISTRY-TURK BIYOKIMYA DERGISI, vol. 42, no. 2, pp. 203–211, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40762.