# Online Structure Learning for Sum-Product Networks with Gaussian Leaves

arXiv: https://arxiv.org/abs/1701.05265 · HTML: https://mlsanity.com/paper/1701.05265/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2017.

## Built on by (most cited first, 4 of 4)

- [Probabilistic Circuits with Constraints via Convex Optimization](https://mlsanity.com/paper/2403.13125/) (2024)
- [Soft Learning Probabilistic Circuits](https://mlsanity.com/paper/2403.14504/) (2024)
- [Hallucination as an Anomaly: Dynamic Intervention via Probabilistic Circuits](https://mlsanity.com/paper/2605.05953/) (2026)
- [Top-Down Bayesian Posterior Sampling for Sum-Product Networks](https://mlsanity.com/paper/2406.12353/) (2024)

## Abstract (arXiv)

Sum-product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which inference is always tractable. Those properties follow from some conditions (i.e., completeness and decomposability) that must be respected by the structure of the network. As a result, it is not easy to specify a valid sum-product network by hand and therefore structure learning techniques are typically used in practice. This paper describes the first online structure learning technique for continuous SPNs with Gaussian leaves. We also introduce an accompanying new parameter learning technique.