Decomposing Parameter Estimation Problems
Abstract
We propose a technique for decomposing the parameter learning problem in Bayesian networks into independent learning problems. Our technique applies to incomplete datasets and exploits variables that are either hidden or observed in the given dataset. We show empirically that the proposed technique can lead to orders-of-magnitude savings in learning time. We explain, analytically and empirically, the reasons behind our reported savings, and compare the proposed technique to related ones that are sometimes used by inference algorithms.
Cite
Text
Refaat et al. "Decomposing Parameter Estimation Problems." Neural Information Processing Systems, 2014.Markdown
[Refaat et al. "Decomposing Parameter Estimation Problems." Neural Information Processing Systems, 2014.](https://mlanthology.org/neurips/2014/refaat2014neurips-decomposing/)BibTeX
@inproceedings{refaat2014neurips-decomposing,
title = {{Decomposing Parameter Estimation Problems}},
author = {Refaat, Khaled S and Choi, Arthur and Darwiche, Adnan},
booktitle = {Neural Information Processing Systems},
year = {2014},
pages = {1565-1573},
url = {https://mlanthology.org/neurips/2014/refaat2014neurips-decomposing/}
}