# Dynamic transformation of prior knowledge into Bayesian models for data streams

arXiv: https://arxiv.org/abs/2003.06123 · HTML: https://mlsanity.com/paper/2003.06123/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization](https://mlsanity.com/paper/2409.19749/) (2024)
- [GloCOM: A Short Text Neural Topic Model via Global Clustering Context](https://mlsanity.com/paper/2412.00525/) (2024)
- [CATCH: A Controllable Theme Detection Framework with Contextualized Clustering and Hierarchical Generation](https://mlsanity.com/paper/2512.21715/) (2025)

## Abstract (arXiv)

We consider how to effectively use prior knowledge when learning a Bayesian model from streaming environments where the data come infinitely and sequentially. This problem is highly important in the era of data explosion and rich sources of precious external knowledge such as pre-trained models, ontologies, Wikipedia, etc. We show that some existing approaches can forget any knowledge very fast. We then propose a novel framework that enables to incorporate the prior knowledge of different forms into a base Bayesian model for data streams. Our framework subsumes some existing popular models for time-series/dynamic data. Extensive experiments show that our framework outperforms existing methods with a large margin. In particular, our framework can help Bayesian models generalize well on extremely short text while other methods overfit. The implementation of our framework is available at https://github.com/bachtranxuan/TPS.git.