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Estimating predictive variance for statistical gas distribution modelling
Örebro University, School of Science and Technology. (AASS Learning Systems Lab)ORCID iD: 0000-0003-0217-9326
Örebro University, School of Science and Technology. (AASS Learning Systems Lab)
Örebro University, School of Science and Technology. (AASS Learning Systems Lab)
2009 (English)In: Olfaction and electronic nose: proceedings / [ed] Matteo Pardo, Giorgio Sberveglieri, Melville, USA: American Institute of Physics (AIP), 2009, p. 65-68Conference paper, Published paper (Refereed)
Abstract [en]

Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work.

Place, publisher, year, edition, pages
Melville, USA: American Institute of Physics (AIP), 2009. p. 65-68
Series
AIP conference proceedings, ISSN 0094-243X ; 1137
Keywords [en]
Gas distribution modelling, gas sensing, mobile robot olfaction, density estimation, model evaluation
National Category
Engineering and Technology Computer Sciences Chemical Sciences
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-8443DOI: 10.1063/1.3156628ISI: 000268929400014Scopus ID: 2-s2.0-70450162840ISBN: 978-0-7354-0674-2 (print)OAI: oai:DiVA.org:oru-8443DiVA, id: diva2:274903
Conference
13th International Symposium on Olfaction and the Electronic Nose, Brescia, Italy, April 15-17, 2009
Projects
EU FP6 STREP DustbotEU FP7 DiademAvailable from: 2009-11-08 Created: 2009-11-02 Last updated: 2018-01-12Bibliographically approved

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Lilienthal, Achim J.Asadi, SaharReggente, Matteo

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