Back to Search
Start Over
Bayesian inference for latent chain graphs
- Source :
- Foundations of Data Science. 2:35-54
- Publication Year :
- 2020
- Publisher :
- American Institute of Mathematical Sciences (AIMS), 2020.
-
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.
Details
- ISSN :
- 26398001
- Volume :
- 2
- Database :
- OpenAIRE
- Journal :
- Foundations of Data Science
- Accession number :
- edsair.doi...........db3b5e91ec53e0500fbe7ec3323f8f6d