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dc.contributor.author Ajoodha, R
dc.contributor.author Rosman, Benjamin S
dc.date.accessioned 2018-08-16T09:14:52Z
dc.date.available 2018-08-16T09:14:52Z
dc.date.issued 2018-02
dc.identifier.citation Ajoodha, R. and Rosman, B. 2018. Learning the influence structure between partially observed stochastic processes using IoT sensor data. SmartIoT: AI Enhanced IoT Data Processing for Intelligent Applications at AAAI-18, Workshop at the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), 2-7 February 2018, Hilton New Orleans Riverside, New Orleans, Louisiana, USA en_US
dc.identifier.uri https://aaai.org/ocs/index.php/WS/AAAIW18/paper/view/16840/15567
dc.identifier.uri https://aaai.org/ocs/index.php/WS/AAAIW18/schedConf/presentations
dc.identifier.uri http://hdl.handle.net/10204/10367
dc.description Paper presented at SmartIoT: AI Enhanced IoT Data Processing for Intelligent Applications at AAAI-18, Workshop at the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), 2-7 February 2018, Hilton New Orleans Riverside, New Orleans, Louisiana, USA en_US
dc.description.abstract The recent widespread of availability of sensors, as part of the IoT, presents the opportunity to learn the properties of compound distributions in practical applications. Understanding temporal distributions by observations collected from the IoT can advance many intelligent applications. In this paper we develop an algorithm to learn influence between stochastic processes using observations obtained from the IoT. The proposed method learns these processes using temporal models independently, and then attempts to recover the underlying distribution of influence between them. Experimental results are provided which demonstrate the effectiveness of our method. This approach is useful in applications that require an understanding of how partially observed high-level processes can influence each other given a set of observations at different times. en_US
dc.language.iso en en_US
dc.publisher AAAI en_US
dc.relation.ispartofseries Worklist;20910
dc.subject Influence structure en_US
dc.subject Stochastic processes en_US
dc.subject IoT en_US
dc.subject Bayesian networks en_US
dc.title Learning the influence structure between partially observed stochastic processes using IoT sensor data en_US
dc.type Presentation en_US


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