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Bayesian network structure learning for the uncertain experimentalist : with applications to network biology Eaton, Daniel James
Abstract
In this work, we address both the computational and modeling aspects of Bayesian network structure learning. Several recent algorithms can handle large networks by operating on the space of variable orderings, but for technical reasons they cannot compute many interesting structural features and require the use of a restrictive prior. We introduce a novel MCMC method that utilizes the deterministic output of the exact structure learning algorithm of Koivisto and Sood to construct a fast-mixing proposal on the space of DAGs. We show that in addition to fixing the order-space algorithms' shortcomings, our method outperforms other existing samplers on real datasets by delivering more accurate structure and higher predictive likelihoods in less compute time. Next, we discuss current models of intervention and propose a novel approach named the uncertain intervention model, whereby the targets of an intervention can be learned in parallel to the graph's causal structure. We validate our model experimentally using synthetic data generated from known ground truth. We then apply our model to two biological datasets that have been previously analyzed using Bayesian networks. On the T-cell dataset of Sachs et al. we show that the uncertain intervention model is able to better model the density of the data compared to previous techniques, while on the ALL dataset of Yeoh et al. we demonstrate that our method can be used to directly estimate the genetic effects of the disease.
Item Metadata
Title |
Bayesian network structure learning for the uncertain experimentalist : with applications to network biology
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2007
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Description |
In this work, we address both the computational and modeling aspects of
Bayesian network structure learning. Several recent algorithms can handle
large networks by operating on the space of variable orderings, but for technical
reasons they cannot compute many interesting structural features and
require the use of a restrictive prior. We introduce a novel MCMC method that
utilizes the deterministic output of the exact structure learning algorithm of
Koivisto and Sood to construct a fast-mixing proposal on the space of DAGs.
We show that in addition to fixing the order-space algorithms' shortcomings,
our method outperforms other existing samplers on real datasets by delivering
more accurate structure and higher predictive likelihoods in less compute time.
Next, we discuss current models of intervention and propose a novel approach
named the uncertain intervention model, whereby the targets of an intervention
can be learned in parallel to the graph's causal structure. We validate our
model experimentally using synthetic data generated from known ground truth.
We then apply our model to two biological datasets that have been previously
analyzed using Bayesian networks. On the T-cell dataset of Sachs et al. we
show that the uncertain intervention model is able to better model the density
of the data compared to previous techniques, while on the ALL dataset of Yeoh
et al. we demonstrate that our method can be used to directly estimate the
genetic effects of the disease.
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Genre | |
Type | |
Language |
eng
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Date Available |
2011-02-22
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0052083
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.