Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting
Name
6830-beyond-normality-learning-sparse-probabilistic-graphical-models-in-the-non-gaussian-setting.pdf
Description
Published version
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444.15 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Morrison, Rebecca E.
Baptista, Ricardo Miguel
Marzouk, Youssef M
Date Issued
December 2017
Journal
31st Conference on Neural Information Processing Systems (NIPS 2017)
Publisher
Curran Associates, Inc.
Citation
Morrison, Rebecca E., Ricardo Baptista and Youssef Marzouk. “Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting.” Paper presented at the 31st Conference on Neural Information Processing Systems (NIPS 2017, Long Beach, CA, Dec. 4-9 2017, Curran Associates, Inc. © 2017 The Author(s)
Version
Final published version
Abstract
We present an algorithm to identify sparse dependence structure in continuous and non-Gaussian probability distributions, given a corresponding set of data. The conditional independence structure of an arbitrary distribution can be represented as an undirected graph (or Markov random field), but most algorithms for learning this structure are restricted to the discrete or Gaussian cases. Our new approach allows for more realistic and accurate descriptions of the distribution in question, and in turn better estimates of its sparse Markov structure. Sparsity in the graph is of interest as it can accelerate inference, improve sampling methods, and reveal important dependencies between variables. The algorithm relies on exploiting the connection between the sparsity of the graph and the sparsity of transport maps, which deterministically couple one probability measure to another.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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