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Counting directed acyclic graphs and...

Flores, Nicandro.

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Counting directed acyclic graphs and its application to Monte Carlo learning of Bayesian networks.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Counting directed acyclic graphs and its application to Monte Carlo learning of Bayesian networks./
Author:	Flores, Nicandro.
Description:	47 p.
Notes:	Adviser: Jem N. Corcoran.
Contained By:	Masters Abstracts International46-03.
Subject:	Artificial Intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1447692
ISBN:	9780549341789

Counting directed acyclic graphs and its application to Monte Carlo learning of Bayesian networks.
Flores, Nicandro.

Counting directed acyclic graphs and its application to Monte Carlo learning of Bayesian networks. - 47 p.

Adviser: Jem N. Corcoran.

Thesis (M.A.)--University of Colorado at Boulder, 2007.

Bayesian networks are convenient graphical expressions for high dimensional probability distributions representing complex relationships between a large number of random variables. A Bayesian network is a directed acyclic graph (DAG) consisting of nodes which represent random variables and arrows which correspond to probabilistic dependencies between them. There has been a great deal of interest in recent years on the problem of learning the structure of Bayesian networks from data. Much of this interest has been driven by the study of genetic regulatory networks in molecular biology and the modeling of many problems in machine learning concerning a range of areas from bioinformatics to document learning. One method for learning DAGs from data is to maximize the probability that a proposed DAG comes from the observed data. Maximizing this probability by brute force is not feasible, instead we use algorithms that step through the state space of DAGs using a type of random walk. In this thesis we give several DAG manipulation and counting formulas that allow such algorithms to move through the state space of DAGs with extreme efficiency.

ISBN: 9780549341789Subjects--Topical Terms:

769149
Artificial Intelligence.

Counting directed acyclic graphs and its application to Monte Carlo learning of Bayesian networks.
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035 $a (UMI)AAI1447692
035 $a AAI1447692
040 $a UMI $c UMI
100 1 $a Flores, Nicandro. $3 1282508
245 1 0 $a Counting directed acyclic graphs and its application to Monte Carlo learning of Bayesian networks.
300 $a 47 p.
500 $a Adviser: Jem N. Corcoran.
500 $a Source: Masters Abstracts International, Volume: 46-03, page: 1537.
502 $a Thesis (M.A.)--University of Colorado at Boulder, 2007.
520 $a Bayesian networks are convenient graphical expressions for high dimensional probability distributions representing complex relationships between a large number of random variables. A Bayesian network is a directed acyclic graph (DAG) consisting of nodes which represent random variables and arrows which correspond to probabilistic dependencies between them. There has been a great deal of interest in recent years on the problem of learning the structure of Bayesian networks from data. Much of this interest has been driven by the study of genetic regulatory networks in molecular biology and the modeling of many problems in machine learning concerning a range of areas from bioinformatics to document learning. One method for learning DAGs from data is to maximize the probability that a proposed DAG comes from the observed data. Maximizing this probability by brute force is not feasible, instead we use algorithms that step through the state space of DAGs using a type of random walk. In this thesis we give several DAG manipulation and counting formulas that allow such algorithms to move through the state space of DAGs with extreme efficiency.
590 $a School code: 0051.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Mathematics. $3 515831
690 $a 0405
690 $a 0715
690 $a 0800
710 2 $a University of Colorado at Boulder. $b Mathematics. $3 1032090
773 0 $t Masters Abstracts International $g 46-03.
790 $a 0051
790 1 0 $a Corcoran, Jem N., $e advisor
790 1 0 $a Gustafson, Karl E. $e committee member
790 1 0 $a Kuznetsov, Sergei E. $e committee member
791 $a M.A.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1447692