Bayesian hierarchical model for large-scale covariance matrix estimation.
Many bioinformatics problems implicitly depend on estimating large-scale covariance matrix. The traditional approaches tend to give rise to high variance and low accuracy due to "overfitting." We cast the large-scale covariance matrix estimation problem into the Bayesian hierarchical model framework, and introduce dependency between covariance parameters. We demonstrate the advantages of our approaches over the traditional approaches using simulations and OMICS data analysis.
Stowers Institute for Medical Research, 1000 E. 50th Street, Kansas City, MO 64112, USA. doz@stowers-institute.org
Dongxiao
D
Zhu
Alfred O
AO
Hero
D001499:Bayes Theorem; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016001:Confidence Intervals; D005690:Galactose / Q000378:metabolism; D008957:Models, Genetic
D005690:Galactose