Learning Heterogeneous Hidden Markov Random Fields
Abstract
Hidden Markov random fields (HMRFs) are conventionally assumed to be homogeneous in the sense that the potential functions are invariant across different sites. However in some biological applications, it is desirable to make HMRFs heterogeneous, especially when there exists some background knowledge about how the potential functions vary. We formally define heterogeneous HMRFs and propose an EM algorithm whose M-step combines a contrastive divergence learner with a kernel smoothing step to incorporate the background knowledge. Simulations show that our algorithm is effective for learning heterogeneous HMRFs and outperforms alternative binning methods. We learn a heterogeneous HMRF in a real-world study.
Cite
Text
Liu et al. "Learning Heterogeneous Hidden Markov Random Fields." International Conference on Artificial Intelligence and Statistics, 2014.Markdown
[Liu et al. "Learning Heterogeneous Hidden Markov Random Fields." International Conference on Artificial Intelligence and Statistics, 2014.](https://mlanthology.org/aistats/2014/liu2014aistats-learning/)BibTeX
@inproceedings{liu2014aistats-learning,
title = {{Learning Heterogeneous Hidden Markov Random Fields}},
author = {Liu, Jie and Zhang, Chunming and Burnside, Elizabeth S. and Page, David},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2014},
pages = {576-584},
url = {https://mlanthology.org/aistats/2014/liu2014aistats-learning/}
}