LEADER 04312nam 22006495 450 001 9910485008003321 005 20251116220557.0 010 $a3-030-27749-6 024 7 $a10.1007/978-3-030-27749-9 035 $a(CKB)4100000009759040 035 $a(MiAaPQ)EBC6114341 035 $a(DE-He213)978-3-030-27749-9 035 $a(PPN)24376944X 035 $a(EXLCZ)994100000009759040 100 $a20191107d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEnhanced Bayesian Network Models for Spatial Time Series Prediction $eRecent Research Trend in Data-Driven Predictive Analytics /$fby Monidipa Das, Soumya K. Ghosh 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (xxiii, 149 pages) $cillustrations 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v858 311 08$a3-030-27748-8 320 $aIncludes bibliographical references and indexes. 327 $aIntroduction -- Standard Bayesian Network Models for Spatial Time Series Prediction -- Bayesian Network with added Residual Correction Mechanism -- Spatial Bayesian Network -- Semantic Bayesian Network -- Advanced Bayesian Network Models with Fuzzy Extension -- Comparative Study of Parameter Learning Complexity -- Spatial Time Series Prediction using Advanced BN Models? An Application Perspective -- Summary and Future Research. 330 $aThis research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science. The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v858 606 $aComputational intelligence 606 $aEngineering?Data processing 606 $aComputational complexity 606 $aEngineering mathematics 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aData Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T11040 606 $aComplexity$3https://scigraph.springernature.com/ontologies/product-market-codes/T11022 606 $aEngineering Mathematics$3https://scigraph.springernature.com/ontologies/product-market-codes/T11030 615 0$aComputational intelligence. 615 0$aEngineering?Data processing. 615 0$aComputational complexity. 615 0$aEngineering mathematics. 615 14$aComputational Intelligence. 615 24$aData Engineering. 615 24$aComplexity. 615 24$aEngineering Mathematics. 676 $a519.542 676 $a519.55 (edition:23) 700 $aDas$b Monidipa$4aut$4http://id.loc.gov/vocabulary/relators/aut$0976150 702 $aGhosh$b Soumya K.$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910485008003321 996 $aEnhanced Bayesian Network Models for Spatial Time Series Prediction$92223246 997 $aUNINA