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FastInf: An Efficient Approximate Inference Library

Authors:

Ariel Jaimovich,

Gal ElidanAuthors Info & Claims

The Journal of Machine Learning Research, Volume 11

Pages 1733 - 1736

Published: 01 August 2010 Publication History

Abstract

The FastInf C++ library is designed to perform memory and time efficient approximate inference in large-scale discrete undirected graphical models. The focus of the library is propagation based approximate inference methods, ranging from the basic loopy belief propagation algorithm to propagation based on convex free energies. Various message scheduling schemes that improve on the standard synchronous or asynchronous approaches are included. Also implemented are a clique tree based exact inference, Gibbs sampling, and the mean field algorithm. In addition to inference, FastInf provides parameter estimation capabilities as well as representation and learning of shared parameters. It offers a rich interface that facilitates extension of the basic classes to other inference and learning methods.

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Cited By

Jiang JWen ZMansoor AMian ABagchi SZhang Y(2024)Fast inference for probabilistic graphical modelsProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3691998(95-110)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3691998
Refaat KDarwiche A(2015)Data compression for learning MRF parametersProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832747.2832777(3784-3790)Online publication date: 25-Jul-2015
https://dl.acm.org/doi/10.5555/2832747.2832777
Lowd DRooshenas A(2015)The Libra toolkit for probabilistic modelsThe Journal of Machine Learning Research10.5555/2789272.291207716:1(2459-2463)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.5555/2789272.2912077
Show More Cited By

Index Terms

FastInf: An Efficient Approximate Inference Library
1. Computing methodologies
  1. Machine learning
  2. Modeling and simulation
    1. Model development and analysis
2. Mathematics of computing
  1. Mathematical software

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cover image The Journal of Machine Learning Research

The Journal of Machine Learning Research Volume 11, Issue

3/1/2010

3637 pages

ISSN:1532-4435

EISSN:1533-7928

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JMLR.org

Publication History

Published: 01 August 2010

Published in JMLR Volume 11

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Cited By

Jiang JWen ZMansoor AMian ABagchi SZhang Y(2024)Fast inference for probabilistic graphical modelsProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3691998(95-110)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3691998
Refaat KDarwiche A(2015)Data compression for learning MRF parametersProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832747.2832777(3784-3790)Online publication date: 25-Jul-2015
https://dl.acm.org/doi/10.5555/2832747.2832777
Lowd DRooshenas A(2015)The Libra toolkit for probabilistic modelsThe Journal of Machine Learning Research10.5555/2789272.291207716:1(2459-2463)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.5555/2789272.2912077
Hazan TPeng JShashua A(2012)Tightening fractional covering upper bounds on the partition function for high-order region graphsProceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence10.5555/3020652.3020692(356-366)Online publication date: 14-Aug-2012
https://dl.acm.org/doi/10.5555/3020652.3020692
Karayev SBaumgartner TFritz MDarrell T(2012)Timely object recognitionProceedings of the 26th International Conference on Neural Information Processing Systems - Volume 110.5555/2999134.2999234(890-898)Online publication date: 3-Dec-2012
https://dl.acm.org/doi/10.5555/2999134.2999234
Mooij J(2010)libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical ModelsThe Journal of Machine Learning Research10.5555/1756006.185992511(2169-2173)Online publication date: 1-Aug-2010
https://dl.acm.org/doi/10.5555/1756006.1859925

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