NDLI: Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

Content Provider	IEEE Xplore Digital Library
Author	Yunfei Xu Jongeun Choi Dass, S. Maiti, T.
Copyright Year	2012
Description	Author affiliation: Dept. of Stat. & Probability, Michigan State Univ., East Lansing, MI, USA (Dass, S.; Maiti, T.) \|\| Depts. of Mech. Eng. & Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA (Jongeun Choi) \|\| Dept. of Mech. Eng., Michigan State Univ., East Lansing, MI, USA (Yunfei Xu)
Abstract	In this paper, we consider the problem of predicting a large scale spatial field using successive noisy measurements obtained by mobile sensing agents. The physical spatial field of interest is discretized and modeled by a Gaussian Markov random field (GMRF) with unknown hyperparameters. From a Bayesian perspective, we design a sequential prediction algorithm to exactly compute the predictive inference of the random field. The prediction algorithm correctly takes into account the uncertainty in hyperparameters in a Bayesian way and also is scalable to be usable for the mobile sensor networks with limited resources. An adaptive sampling strategy is also designed for mobile sensing agents to find the most informative locations in taking future measurements in order to minimize the prediction error and the uncertainty in hyperparameters simultaneously. The effectiveness of the proposed algorithms is illustrated by a numerical experiment.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	2171
Ending Page	2176
File Size	444063
Page Count	6
File Format	PDF
ISBN	9781457710957
ISSN	07431619
e-ISBN	9781457710964
e-ISBN	9781467321020
DOI	10.1109/ACC.2012.6315013
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Bayesian methods Prediction algorithms Mobile communication Algorithm design and analysis Covariance matrix Vectors Inference algorithms
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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

Bayesian prediction and adaptive sampling algorithms for mobile sensor networks

Learning Bayesian networks by learning decomposable Markov networks first

Spatial Prediction With Mobile Sensor Networks Using Gaussian Process Regression Based on Gaussian Markov Random Fields

Fully Bayesian Prediction Algorithms for Mobile Robotic Sensors under Uncertain Localization Using Gaussian Markov Random Fields

Sequential Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks

Fully Bayesian simultaneous localization and spatial prediction using Gaussian Markov random fields (GMRFs)

Signal Modeling and Classification Using a Robust Latent Space Model Based on $t$ Distributions

Video Super-Resolution Using Generalized Gaussian Markov Random Fields

Statistical Inferences by Gaussian Markov Random Fields on Complex Networks

Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

Similar Documents

Bayesian prediction and adaptive sampling algorithms for mobile sensor networks

Learning Bayesian networks by learning decomposable Markov networks first

Spatial Prediction With Mobile Sensor Networks Using Gaussian Process Regression Based on Gaussian Markov Random Fields

Fully Bayesian Prediction Algorithms for Mobile Robotic Sensors under Uncertain Localization Using Gaussian Markov Random Fields

Sequential Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks

Fully Bayesian simultaneous localization and spatial prediction using Gaussian Markov random fields (GMRFs)

Signal Modeling and Classification Using a Robust Latent Space Model Based on $t$ Distributions

Video Super-Resolution Using Generalized Gaussian Markov Random Fields

Statistical Inferences by Gaussian Markov Random Fields on Complex Networks

Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields