# Bayesian Inference for Latent Chain Graphs

## TL;DR

This paper develops a Bayesian inference framework using sequential Monte Carlo methods for AMP Gaussian chain graph models, which are useful in biological and financial applications but pose computational challenges.

## Contribution

It introduces a novel SMC-based approach to handle prior modeling and inference in complex AMP Gaussian chain graph models.

## Key findings

- Effective inference demonstrated on simulated data
- Successful application to real-world case studies
- Addresses computational challenges in chain graph models

## 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.