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西安统计与数据科学论坛第三场报告通知
威尼斯官网 : 2020-10-09     点击量:

讲座人 Jian Kang 教授,University of Michigan

讲座题目Bayesian network marker selection via the thresholded graph Laplacian Gaussian prior

讲座时间:2020年10月14日,星期三,10:00-12:00

讲座概况Selecting network markers becomes increasingly important in imaging and genomics. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this talk, I will introduce a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and real data analyses.

讲座人概况Dr. Jian Kang is a Professor of Biostatistics at the University of Michigan. His main research interests are developing statistical methods and theory for large-scale complex data analysis with focuses on Bayesian models, machine learning, imaging and metabolomics. He has over 70 publications in leading statistical journals and medical journals. Dr. Kang currently serves as the Associate Editor of the Journal of the American Statistical Association, Biometrics and Statistics in Medicine.

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