# Structure learning of Bayesian networks involving cyclic structures

arXiv: https://arxiv.org/abs/1906.04992 · HTML: https://mlsanity.com/paper/1906.04992/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [On the Foundations of Cycles in Bayesian Networks](https://mlsanity.com/paper/2301.08608/) (2023)

## Abstract (arXiv)

Many biological networks include cyclic structures. In such cases, Bayesian networks (BNs), which must be acyclic, are not sound models for structure learning. Dynamic BNs can be used but require relatively large time series data. We discuss an alternative model that embeds cyclic structures within acyclic BNs, allowing us to still use the factorization property and informative priors on network structure. We present an implementation in the linear Gaussian case, where cyclic structures are treated as multivariate nodes. We use a Markov Chain Monte Carlo algorithm for inference, allowing us to work with posterior distribution on the space of graphs.