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arXiv · 1908.04002

Bayesian Inference for Latent Chain Graphs

Deng Lu·Maria De Iorio·Ajay Jasra·Gary L. Rosner
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Abstract

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.

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Deng Lu, Maria De Iorio, Ajay Jasra, Gary L. Rosner. 2019-08-12. Bayesian Inference for Latent Chain Graphs. https://arxiv.org/abs/1908.04002

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