An Approach for Fast Score Computation in Bayesian Network Structure Learning Over Large-Scale Distributed Data

On a fundamental level, Bayes’ theorem enables us to utilize prior knowledge to determine the probability of an event. In consequence, its suitability for probabilistic reasoning has lead to its employment in probabilistic graphical modeling and the inception of Bayesian networks. The field is saturated with techniques to learn the structure of a Bayesian network (also known as Bayes network). Nevertheless, most of the techniques struggle when the number of variables (or network nodes) and the input data grow drastically. At that point, parallel distributed processing is the best alternative to alleviate the computational complexity of this problem. To this end, we propose a gossip-based distributed score computation approach called DiSC that is used to compute the sufficient statistics of families of variables in order to accelerate the structure learning process of Bayesian networks. We show that DiSC can significantly outperform map-reduce style score computations executed by the distributed computation framework Apache Spark on a variety of synthetic and real datasets with a low accuracy trade-off.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computer Science, Computer Networking and Communication Systems (UMKC)

Grantor dc:publisher

University of Missouri -- Kansas City

Year dc:date.issued

2018

Author dc:creator

• Katib, Anas Adnan

Advisor dc:contributor.advisor

• Rao, Praveen R.

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/10355/67033

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/67033