Bayesian Inference for Latent Chain Graphs
Methodology
2019-08-13 v1
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.
Cite
@article{arxiv.1908.04002,
title = {Bayesian Inference for Latent Chain Graphs},
author = {Deng Lu and Maria De Iorio and Ajay Jasra and Gary L. Rosner},
journal= {arXiv preprint arXiv:1908.04002},
year = {2019}
}