# Tractable Learning and Inference for Large-Scale Probabilistic Boolean Networks

arXiv: https://arxiv.org/abs/1801.07693 · HTML: https://mlsanity.com/paper/1801.07693/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2018.

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

- [Inferring probabilistic Boolean networks from steady-state gene data samples](https://mlsanity.com/paper/2211.05935/) (2022)

## Abstract (arXiv)

Probabilistic Boolean Networks (PBNs) have been previously proposed so as to gain insights into complex dy- namical systems. However, identification of large networks and of the underlying discrete Markov Chain which describes their temporal evolution, still remains a challenge. In this paper, we introduce an equivalent representation for the PBN, the Stochastic Conjunctive Normal Form (SCNF), which paves the way to a scalable learning algorithm and helps predict long- run dynamic behavior of large-scale systems. Moreover, SCNF allows its efficient sampling so as to statistically infer multi- step transition probabilities which can provide knowledge on the activity levels of individual nodes in the long run.