# Bayesian Network Models for Incomplete and Dynamic Data

## TL;DR

This paper reviews how Bayesian networks can effectively model complex, dynamic, and incomplete data, emphasizing their interpretability and applicability in real-world scenarios.

## Contribution

It provides a comprehensive overview of Bayesian network models tailored for dynamic and incomplete data, extending their traditional static, fully observed data applications.

## Key findings

- Bayesian networks effectively model dynamic data.
- They handle incomplete observations well.
- Their interpretability benefits real-world applications.

## Abstract

Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the comparatively simple case of completely observed, static data, which has received the most attention in the literature, in this paper we will review how Bayesian networks can model dynamic data and data with incomplete observations. Such data are the norm at the forefront of research and in practical applications, and Bayesian networks are uniquely positioned to model them due to their explainability and interpretability.