Bayesian network structure learning for the uncertain experimentalist : with applications to network biology
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Eaton, Daniel James
issued: 2011-02-22T21:25:35Z
issued: 2007
related degree: Master of Science - MSc
degree grantor: University of British Columbia
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.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/31610
note: Bayesian network structure learning for the uncertain experimentalist W i t h applications to network biology by Daniel James Eaton B.Sc, The University of British Columbia, 2005 A THESIS SUBMITTED IN PARTIAL F U L F I L M E N T OF THE REQUIREMENTS FOR THE D E G R E E OF Master of Science in The Faculty of Graduate Studies (Computer Science) The University Of British Columbia June, 2007 © Daniel James Eaton 2007 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 tech-nical reasons they cannot compute many interesting structural features and require the use of a restrictive prior. We introduce a novel M C M C 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 interven-tion 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 A L L dataset of Yeoh et al. we demonstrate that our method can be used to directly estimate the genetic effects of the disease. ii Table of Contents Abstract 1 1 Table of Contents n i List of Tables v List …
has type: Thesis/Dissertation
is shown at: 10.14288/1.0052083
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
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.
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/31610