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双重广义线性模型：似然与贝叶斯方法
Edilberto Cepeda-Cuervo. Boca Raton, FL: Chapman & Hall/CRC Press, 2026, xiii + 146 pp., $159.99(e-book), $66.99(H), ISBN: 978-1-003-68741-2(e-book), ISBN: 978-1-041-16997-0(H).In generalized linea...
异质数据的协变量辅助贝叶斯图学习
In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no extra variables affecting the conditional independence. In modern genomic datasets, there is an abundance of auxiliary information, which often gets under-utilized in determining the joint dependency structure. In this article, we consider a Bayesian approach to model undirected graphs underlying heterogeneous multivariate observations with additional assistance from covariates. Building…
用于电子健康记录表型识别的贝叶斯双重特征分配
Electronic health records (EHR) provide opportunities for deeper understanding of human phenotypes - in our case, latent disease - based on statistical modeling. We propose a categorical matrix factorization method to infer latent diseases from EHR data. A latent disease is defined as an unknown biological aberration that causes a set of common symptoms for a group of patients. The proposed approach is based on a novel double feature allocation model which simultaneously…
贝叶斯层级变稀疏回归及癌症蛋白基因组学应用
Identifying patient-specific prognostic biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer. In this article, we propose a novel regression framework, Bayesian hierarchical varying-sparsity regression (BEHAVIOR) models to select clinically relevant disease markers by integrating proteogenomic (proteomic+genomic) and clinical data. Our methods allow flexible modeling of protein–gene…
贝叶斯图回归
We consider the problem of modeling conditional independence structures in heterogeneous data in the presence of additional subject-level covariates - termed Graphical Regression. We propose a novel specification of a conditional (in)dependence function of covariates - which allows the structure of a directed graph to vary flexibly with the covariates; imposes sparsity in both edge and covariate selection; produces both subject-specific and predictive graphs; and is…
稀疏多维图模型的统一贝叶斯框架
Yang Niab, Francesco C. Stingoac* & Veerabhadran Baladandayuthapaniaa Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TXb Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, TXc Dipartimento di Statistica, Informatica, Applicazioni “G.Parenti,” University of Florence, Florence, ItalyCONTACT Francesco C. Stingo fstingo@mdanderson.org Dipartimento di Statistica, Informatica, Applicazioni “G.Parenti,”…