Adaptive inference on general graphical models

Umut A. Acar, Alexander T. Ihler, Ramgopal R. Mettu, Özgür Sümer
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:1-8, 2008.

Abstract

Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of adaptive inference is to take advantage of what is preserved in the model and perform inference more rapidly than from scratch. In this paper, we describe techniques for adaptive inference on general graphs that support marginal computation and updates to the conditional probabilities and dependencies in logarithmic time. We give experimental results for an implementation of our algorithm, and demonstrate its potential performance benefit in the study of protein structure.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR6-acar08a, title = {Adaptive inference on general graphical models}, author = {Acar, Umut A. and Ihler, Alexander T. and Mettu, Ramgopal R. and S\"{u}mer, \"{O}zg\"{u}r}, booktitle = {Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence}, pages = {1--8}, year = {2008}, editor = {McAllester, David A. and Myllymäki, Petri}, volume = {R6}, series = {Proceedings of Machine Learning Research}, month = {09--12 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r6/main/assets/acar08a/acar08a.pdf}, url = {https://proceedings.mlr.press/r6/acar08a.html}, abstract = {Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of adaptive inference is to take advantage of what is preserved in the model and perform inference more rapidly than from scratch. In this paper, we describe techniques for adaptive inference on general graphs that support marginal computation and updates to the conditional probabilities and dependencies in logarithmic time. We give experimental results for an implementation of our algorithm, and demonstrate its potential performance benefit in the study of protein structure.}, note = {Reissued by PMLR on 09 October 2024.} }
Endnote
%0 Conference Paper %T Adaptive inference on general graphical models %A Umut A. Acar %A Alexander T. Ihler %A Ramgopal R. Mettu %A Özgür Sümer %B Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2008 %E David A. McAllester %E Petri Myllymäki %F pmlr-vR6-acar08a %I PMLR %P 1--8 %U https://proceedings.mlr.press/r6/acar08a.html %V R6 %X Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of adaptive inference is to take advantage of what is preserved in the model and perform inference more rapidly than from scratch. In this paper, we describe techniques for adaptive inference on general graphs that support marginal computation and updates to the conditional probabilities and dependencies in logarithmic time. We give experimental results for an implementation of our algorithm, and demonstrate its potential performance benefit in the study of protein structure. %Z Reissued by PMLR on 09 October 2024.
APA
Acar, U.A., Ihler, A.T., Mettu, R.R. & Sümer, Ö.. (2008). Adaptive inference on general graphical models. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R6:1-8 Available from https://proceedings.mlr.press/r6/acar08a.html. Reissued by PMLR on 09 October 2024.

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