# Learning the Structure of Dynamic Probabilistic Networks

arXiv: https://arxiv.org/abs/1301.7374 · HTML: https://mlsanity.com/paper/1301.7374/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2013.

## Built on by (most cited first, 12 of 12)

- [Causal Discovery from Temporal Data: An Overview and New Perspectives](https://mlsanity.com/paper/2303.10112/) (2023)
- [DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets](https://mlsanity.com/paper/2302.04178/) (2023)
- [DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena](https://mlsanity.com/paper/2303.06556/) (2023)
- [A Survey on Semantic Processing Techniques](https://mlsanity.com/paper/2310.18345/) (2023)
- [How do Machine Learning Models Change?](https://mlsanity.com/paper/2411.09645/) (2024)
- [Automated Dynamic Bayesian Networks for Predicting Acute Kidney Injury Before Onset](https://mlsanity.com/paper/2304.10175/) (2023)
- [A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations](https://mlsanity.com/paper/2311.00923/) (2023)
- [Transformer Is Inherently a Causal Learner](https://mlsanity.com/paper/2601.05647/) (2026)
- [Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesian Network Structure Learning](https://mlsanity.com/paper/2312.01739/) (2023)
- [Shylock: Causal Discovery in Multivariate Time Series based on Hybrid Constraints](https://mlsanity.com/paper/2510.21181/) (2025)
- [Extending the application of dynamic Bayesian networks in calculating market risk: Standard and stressed expected shortfall](https://mlsanity.com/paper/2512.12334/) (2025)
- [Proactive Dialogue Model with Intent Prediction](https://mlsanity.com/paper/2604.27379/) (2026)

## Abstract (arXiv)

Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend structure scoring rules for standard probabilistic networks to the dynamic case, and show how to search for structure when some of the variables are hidden. Finally, we examine two applications where such a technology might be useful: predicting and classifying dynamic behaviors, and learning causal orderings in biological processes. We provide empirical results that demonstrate the applicability of our methods in both domains.