Learning local directed acyclic graphs based on multivariate time series data

DOI10.1214/13-AOAS635zbMath1454.62326arXiv1312.0390WikidataQ35086180 ScholiaQ35086180MaRDI QIDQ386754

Wanlu Deng, Hongzhe Li, Zhi Geng

Publication date: 10 December 2013

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1312.0390

zbMATH Keywords

Bayesian networkcomposite likelihood ratio testgenetic networkPCD-PCD algorithm

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Probabilistic graphical models (62H22)

Related Items (1)

Learning local directed acyclic graphs based on multivariate time series data

Uses Software

• R
• GeneNet
• ebdbNet
• TETRAD

Cites Work

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• A characterization of Markov equivalence classes for acyclic digraphs
• Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks
• Bootstrap inference for network construction with an application to a breast cancer microarray study
• An empirical Bayesian method for estimating biological networks from temporal microarray data
• Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks
• Learning Causal Bayesian Network Structures From Experimental Data
• 10.1162/153244303321897717

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