Nonparametric Bayesian Inference on Multivariate Exponential Families
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Roy_Nonparametric Bayesian inference.pdf
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461.29 KB
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Author(s)
Vega-Brown, William R
Doniec, Marek Wojciech
Roy, Nicholas
Date Issued
December 2014
Journal
Proceedings of the 27th International Conference on Neural Information Processing Systems
Publisher
Association for Computing Machinery (ACM)
Citation
Vega-Brown, William, Marek Doniec and Nicholas Roy. "Nonparametric Bayesian inference on multivariate exponential families." 27th International Conference on Neural Information Processing Systems, 8-13 December, 2014, Montreal, Canada, Association for Computing Machinery, 2014.
Version
Author's final manuscript
Abstract
We develop a model by choosing the maximum entropy distribution from the set of models satisfying certain smoothness and independence criteria; we show that inference on this model generalizes local kernel estimation to the context of Bayesian inference on stochastic processes. Our model enables Bayesian inference in contexts when standard techniques like Gaussian process inference are too expensive to apply. Exact inference on our model is possible for any likelihood function from the exponential family. Inference is then highly efficient, requiring only O (log N) time and O (N) space at run time. We demonstrate our algorithm on several problems and show quantifiable improvement in both speed and performance relative to models based on the Gaussian process.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
http://dl.acm.org/citation.cfm?id=2969111
