Estimation and Model Selection for an IDE-Based Spatio-Temporal Model
A state space model of the stochastic spatio-temporal integro-difference equation (IDE) is derived. Based on multidimensional sampling theory, the dimensions of the state space and parameter space of the model are identified from the spatial bandwidth of the system and the support of the redistribut...
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| Veröffentlicht in: | IEEE transactions on signal processing 2009-02, Vol.57 (2), p.482-492 |
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| container_title | IEEE transactions on signal processing |
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| creator | Scerri, K. Dewar, M. Kadirkamanathan, V. |
| description | A state space model of the stochastic spatio-temporal integro-difference equation (IDE) is derived. Based on multidimensional sampling theory, the dimensions of the state space and parameter space of the model are identified from the spatial bandwidth of the system and the support of the redistribution kernel of the IDE. When both the bandwidth and the kernel support are unknown, a method to propose a number of state space and parameter space dimensions is presented. These chosen dimensions result in a number of candidate model structures. Bayesian model selection, making use of Bayes factor, the data augmentation algorithm and importance sampling, is then used to identify the model best suited to represent the data in a maximum a posteriori sense. |
| doi_str_mv | 10.1109/TSP.2008.2008550 |
| format | Article |
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Based on multidimensional sampling theory, the dimensions of the state space and parameter space of the model are identified from the spatial bandwidth of the system and the support of the redistribution kernel of the IDE. When both the bandwidth and the kernel support are unknown, a method to propose a number of state space and parameter space dimensions is presented. These chosen dimensions result in a number of candidate model structures. Bayesian model selection, making use of Bayes factor, the data augmentation algorithm and importance sampling, is then used to identify the model best suited to represent the data in a maximum a posteriori sense.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/TSP.2008.2008550</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Augmentation ; Bandwidth ; Bayes factor ; Bayesian analysis ; Biological system modeling ; Brain modeling ; data augmentation (DA) algorithm ; dynamic spatio-temporal modeling ; Environmental factors ; Exact sciences and technology ; Importance sampling ; Information, signal and communications theory ; integro-difference equations ; Kernel ; Kernels ; Mathematical analysis ; Mathematical models ; Miscellaneous ; Monte Carlo methods ; Organisms ; Predictive models ; Sampling ; Signal processing ; State-space methods ; state-space models ; Systems engineering and theory ; Telecommunications and information theory</subject><ispartof>IEEE transactions on signal processing, 2009-02, Vol.57 (2), p.482-492</ispartof><rights>2009 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2009</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c383t-4793df837bd258cb94b4f6fd747ab260e6eb98aac6d2b95f8ea5900e8f33e5753</citedby><cites>FETCH-LOGICAL-c383t-4793df837bd258cb94b4f6fd747ab260e6eb98aac6d2b95f8ea5900e8f33e5753</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4671054$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4671054$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21172424$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Scerri, K.</creatorcontrib><creatorcontrib>Dewar, M.</creatorcontrib><creatorcontrib>Kadirkamanathan, V.</creatorcontrib><title>Estimation and Model Selection for an IDE-Based Spatio-Temporal Model</title><title>IEEE transactions on signal processing</title><addtitle>TSP</addtitle><description>A state space model of the stochastic spatio-temporal integro-difference equation (IDE) is derived. Based on multidimensional sampling theory, the dimensions of the state space and parameter space of the model are identified from the spatial bandwidth of the system and the support of the redistribution kernel of the IDE. When both the bandwidth and the kernel support are unknown, a method to propose a number of state space and parameter space dimensions is presented. These chosen dimensions result in a number of candidate model structures. Bayesian model selection, making use of Bayes factor, the data augmentation algorithm and importance sampling, is then used to identify the model best suited to represent the data in a maximum a posteriori sense.</description><subject>Applied sciences</subject><subject>Augmentation</subject><subject>Bandwidth</subject><subject>Bayes factor</subject><subject>Bayesian analysis</subject><subject>Biological system modeling</subject><subject>Brain modeling</subject><subject>data augmentation (DA) algorithm</subject><subject>dynamic spatio-temporal modeling</subject><subject>Environmental factors</subject><subject>Exact sciences and technology</subject><subject>Importance sampling</subject><subject>Information, signal and communications theory</subject><subject>integro-difference equations</subject><subject>Kernel</subject><subject>Kernels</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Miscellaneous</subject><subject>Monte Carlo methods</subject><subject>Organisms</subject><subject>Predictive models</subject><subject>Sampling</subject><subject>Signal processing</subject><subject>State-space methods</subject><subject>state-space models</subject><subject>Systems engineering and theory</subject><subject>Telecommunications and information theory</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kc1Lw0AQxYMoWKt3wUsQ1FPqfn8ctUYtVBRawduySWYhJU3qbnvwv3fblB48eNkZZn7vwbxNkkuMRhgjfT-ffYwIQmr3cI6OkgHWDGeISXEce8RpxpX8Ok3OQlgghBnTYpDkeVjXS7uuuza1bZW-dRU06QwaKHcz1_k4TydPefZoA1TpbLWFszksV523TS84T06cbQJc7Osw-XzO5-PXbPr-Mhk_TLOSKrrOmNS0corKoiJclYVmBXPCVZJJWxCBQEChlbWlqEihuVNguUYIlKMUuOR0mNz1vivffW8grM2yDiU0jW2h2wSjJI_3Ii4jefsvSZkmSmERwes_4KLb-DZeYZTAmBKKWYRQD5W-C8GDMysfU_M_BiOzjd_E-M02ebOPP0pu9r42lLZx3rZlHQ46grEkjGytr3quBoDDmgkZf4zRX-tLi6k</recordid><startdate>20090201</startdate><enddate>20090201</enddate><creator>Scerri, K.</creator><creator>Dewar, M.</creator><creator>Kadirkamanathan, V.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| ispartof | IEEE transactions on signal processing, 2009-02, Vol.57 (2), p.482-492 |
| issn | 1053-587X 1941-0476 |
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| subjects | Applied sciences Augmentation Bandwidth Bayes factor Bayesian analysis Biological system modeling Brain modeling data augmentation (DA) algorithm dynamic spatio-temporal modeling Environmental factors Exact sciences and technology Importance sampling Information, signal and communications theory integro-difference equations Kernel Kernels Mathematical analysis Mathematical models Miscellaneous Monte Carlo methods Organisms Predictive models Sampling Signal processing State-space methods state-space models Systems engineering and theory Telecommunications and information theory |
| title | Estimation and Model Selection for an IDE-Based Spatio-Temporal Model |
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