Macroscopic Models of Clique Tree Growth for Bayesian Networks
Description
In clique tree clustering, inference consists of propagation in a clique tree compiled from a Bayesian network. In this paper, we develop an analytical approach to characterizing clique tree growth as a function of increasing Bayesian network connectedness, specifically: (i) the expected number of moral edges in their moral graphs or (ii) the ratio of the number of non-root nodes to the number of root nodes. In experiments, we systematically increase the connectivity of bipartite Bayesian networks, and find that clique tree size growth is well-approximated by Gompertz growth curves. This research improves the understanding of the scaling behavior of clique tree clustering, provides a foundation for benchmarking and developing improved BN inference algorithms, and presents an aid for analytical trade-off studies of tree clustering using growth curves.
**Reference:**
O. J. Mengshoel, "Macroscopic Models of Clique Tree Growth for Bayesian Networks." In Proc. of the 22nd National Conference on Artificial Intelligence (AAAI-07). July 2007, Vancouver, Canada, pp. 1256-1262.
**BibTex Reference:**
@inproceedings{mengshoel07macroscopic,
author = "Mengshoel, O. J.",
title = "Macroscopic Models of Clique Tree Growth for {Bayesian} Networks",
year = "2007",
booktitle = {Proceedings of the Twenty-Second National Conference on Artificial Intelligence (AAAI-07)},
pages = "1256-1262",
address = "Vancouver, British Columbia"
}
Resources
| Name |
Format |
Description |
Link |
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33 |
GrowthV26aaai.pdf |
https://c3.nasa.gov/dashlink/static/media/publication/GrowthV26aaai.pdf |