BAYESIAN INFERENCE FOR LATENT CHAIN GRAPHS
Type
Article
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal, 23955, KSAComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Date
2020-03Permanent link to this record
http://hdl.handle.net/10754/660810Metadata
Show full item recordAbstract
In this article we consider Bayesian inference for partially observed Andersson-Madigan-Perlman (AMP) Gaussian chain graph (CG) models. Such models are of particular interest in applications such as biological networks and financial time series. The model itself features a variety of constraints which make both prior modeling and computational inference challenging. We develop a framework for the aforementioned challenges, using a sequential Monte Carlo (SMC) method for statistical inference. Our approach is illustrated on both simulated data as well as real case studies from university graduation rates and a pharmacokinetics study.Citation
Lu, D., De Iorio, M., Jasra, A., & L. Rosner, G. (2020). Bayesian inference for latent chain graphs. Foundations of Data Science, 2(1), 35–54. https://doi.org/10.3934/fods.2020003Sponsors
We acknowledge the contribution of the study team and participants of the RMP-02/MTN-006 study and thank them for sharing the study data. Partial support for this research came from grant numbers U19 AI060614 and UM1 AI106707 from the U.S. National Institutes of Health. GLR was partially supported by grant P30CA006973 from the U.S. National Cancer Institute.Journal
Foundations of Data SciencearXiv
1908.04002Additional Links
http://aimsciences.org//article/doi/10.3934/fods.2020003Scopus Count
Except where otherwise noted, this item's license is described as Archived with thanks to Foundations of Data Science under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0/