NDLI: MapReduce guided approximate inference over graphical models

Content Provider	IEEE Xplore Digital Library
Author	Haque, A. Chandra, S. Khan, L. Baron, M.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Texas at Dallas, Richardson, TX, USA (Haque, A.; Chandra, S.; Khan, L.) \|\| Dept. of Math. Sci., Univ. of Texas at Dallas, Richardson, TX, USA (Baron, M.)
Abstract	A graphical model represents the data distribution of a data generating process and inherently captures its feature relationships. This stochastic model can be used to perform inference, to calculate posterior probabilities, in various applications such as classification. Exact inference algorithms are known to be intractable on large networks due to exponential time and space complexity. Approximate inference algorithms are instead widely used in practice to overcome this constraint, with a trade off in accuracy. Stochastic sampling is one such method where an approximate probability distribution is empirically evaluated using various sampling techniques. However, these algorithms may still suffer from scalability issues on large and complex networks. To address this challenge, we have designed and implemented several MapReduce based distributed versions of a specific type of approximate inference algorithm called Adaptive Importance Sampling (AIS). We compare and evaluate the proposed approaches using benchmark networks. Experimental result shows that our approach achieves significant scaleup and speedup compared to the sequential algorithm, while achieving similar accuracy asymptotically.
Starting Page	446
Ending Page	453
File Size	821287
Page Count	8
File Format	PDF
ISBN	9781479945184
DOI	10.1109/CIDM.2014.7008702
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Graphical models Monte Carlo methods Computer architecture Approximation algorithms Inference algorithms Approximate Inference Adaptive Importance Sampling Proposals Markov random fields MapReduce
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in