# Transfer learning for nonparametric Bayesian networks

arXiv: https://arxiv.org/abs/2604.01021 · HTML: https://mlsanity.com/paper/2604.01021/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 12 of its 62 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Builds on (most cited first, 12 of 12)

- [Causal Inference and Causal Explanation with Background Knowledge](https://mlsanity.com/paper/1302.4972/) (2013)
- [A review of domain adaptation without target labels](https://mlsanity.com/paper/1901.05335/) (2019)
- [A Survey on Negative Transfer](https://mlsanity.com/paper/2009.00909/) (2020)
- [A survey of Bayesian Network structure learning](https://mlsanity.com/paper/2109.11415/) (2021)
- [Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery](https://mlsanity.com/paper/1702.03877/) (2017)
- [Fusion of Probability Density Functions](https://mlsanity.com/paper/2202.11633/) (2022)
- [Optimal Bayesian Transfer Learning](https://mlsanity.com/paper/1801.00857/) (2018)
- [Properties of Bayesian Belief Network Learning Algorithms](https://mlsanity.com/paper/1302.6792/) (2013)
- [On the Sample Complexity of Learning Bayesian Networks](https://mlsanity.com/paper/1302.3579/) (2013)
- [Semiparametric Bayesian Networks](https://mlsanity.com/paper/2109.03008/) (2021)
- [Transferring model structure in Bayesian transfer learning for Gaussian process regression](https://mlsanity.com/paper/2101.06884/) (2021)
- [A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms](https://mlsanity.com/paper/2109.01377/) (2021)

## Abstract (arXiv)

This paper introduces two transfer learning methodologies for estimating nonparametric Bayesian networks under scarce data. We propose two algorithms, a constraint-based structure learning method, called PC-stable-transfer learning (PCS-TL), and a score-based method, called hill climbing transfer learning (HC-TL). We also define particular metrics to tackle the negative transfer problem in each of them, a situation in which transfer learning has a negative impact on the model's performance. Then, for the parameters, we propose a log-linear pooling approach. For the evaluation, we learn kernel density estimation Bayesian networks, a type of nonparametric Bayesian network, and compare their transfer learning performance with the models alone. To do so, we sample data from small, medium and large-sized synthetic networks and datasets from the UCI Machine Learning repository. Then, we add noise and modifications to these datasets to test their ability to avoid negative transfer. To conclude, we perform a Friedman test with a Bergmann-Hommel post-hoc analysis to show statistical proof of the enhanced experimental behavior of our methods. Thus, PCS-TL and HC-TL demonstrate to be reliable algorithms for improving the learning performance of a nonparametric Bayesian network with scarce data, which in real industrial environments implies a reduction in the required time to deploy the network.