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020			_a9783030277499
024	7		_a10.1007/978-3-030-27749-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA280 _b2020 EB
100	1		_aDas, Monidipa _eautor _9672541
245	1	0	_aEnhanced Bayesian Network Models for Spatial Time Series Prediction _bRecent Research Trend in Data-Driven Predictive Analytics _cby Monidipa Das, Soumya K. Ghosh.
250			_a1st ed. 2020.
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2020.
300			_a1 recurso en línea (XXIII, 149 páginas) _b 67 ilustraciones, 59 ilustraciones a color.
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aStudies in Computational Intelligence _x1860-949X _v858
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_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.
520	3		_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.
988			_aPrimersemestre_2020_Robotics
650		7	_2embne _9141270 _aSeries temporales
700	1		_aGhosh, Soumya K _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _997892
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783030277482
776	0	8	_iPrinted edition: _z9783030277505
776	0	8	_iPrinted edition: _z9783030277512
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-27749-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2020 _dz _ek _zSI