Bayesian source detection and parameter estimation of a plume model based on sensor network measurements
dc.contributor.author | Huang, Chunfeng | en_US |
dc.contributor.author | Hsing, Tailen | en_US |
dc.contributor.author | Cressie, Noel | en_US |
dc.contributor.author | Ganguly, Auroop R. | en_US |
dc.contributor.author | Protopopescu, Vladimir A. | en_US |
dc.contributor.author | Rao, Nageswara S. | en_US |
dc.date.accessioned | 2010-10-06T14:54:26Z | |
dc.date.available | 2011-03-01T16:26:42Z | en_US |
dc.date.issued | 2010-07 | en_US |
dc.identifier.citation | Huang, Chunfeng; Hsing, Tailen; Cressie, Noel; Ganguly, Auroop R.; Protopopescu, Vladimir A.; Rao, Nageswara S. (2010). "Bayesian source detection and parameter estimation of a plume model based on sensor network measurements." Applied Stochastic Models in Business and Industry 26(4): 331-348. <http://hdl.handle.net/2027.42/78051> | en_US |
dc.identifier.issn | 1524-1904 | en_US |
dc.identifier.issn | 1526-4025 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/78051 | |
dc.description.abstract | We consider a network of sensors that measure the intensities of a complex plume composed of multiple absorption–diffusion source components. We address the problem of estimating the plume parameters, including the spatial and temporal source origins and the parameters of the diffusion model for each source, based on a sequence of sensor measurements. The approach not only leads to multiple-source detection, but also the characterization and prediction of the combined plume in space and time. The parameter estimation is formulated as a Bayesian inference problem, and the solution is obtained using a Markov chain Monte Carlo algorithm. The approach is applied to a simulation study, which shows that an accurate parameter estimation is achievable. Copyright © 2010 John Wiley & Sons, Ltd. | en_US |
dc.format.extent | 538406 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | John Wiley & Sons, Ltd. | en_US |
dc.subject.other | Mathematics and Statistics | en_US |
dc.title | Bayesian source detection and parameter estimation of a plume model based on sensor network measurements | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Mathematics | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Statistics, University of Michigan, Ann Arbor, MI, U.S.A. | en_US |
dc.contributor.affiliationother | Department of Statistics, Indiana University, Bloomington, IN, U.S.A. ; Department of Statistics, Indiana University, Bloomington, IN, U.S.A. | en_US |
dc.contributor.affiliationother | Department of Statistics, The Ohio State University, Columbus, OH, U.S.A. | en_US |
dc.contributor.affiliationother | Oak Ridge National Laboratory, Oak Ridge, TN, U.S.A. | en_US |
dc.contributor.affiliationother | Oak Ridge National Laboratory, Oak Ridge, TN, U.S.A. | en_US |
dc.contributor.affiliationother | Oak Ridge National Laboratory, Oak Ridge, TN, U.S.A. | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/78051/1/859_ftp.pdf | |
dc.identifier.doi | 10.1002/asmb.859 | en_US |
dc.identifier.source | Applied Stochastic Models in Business and Industry | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.