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This is the first text that throws light on the recent advancements in developing enhanced Bayesian network (BN) models to address the various challenges in spatial time series prediction
The monograph covers both theoretical and empirical aspects of a number of enhanced Bayesian network models, in a lucid, precise, and highly comprehensive manner
The monograph includes plenty of illustrative examples and proofs which will immensely help the reader to better understand the working principles of the enhanced BN models.
The open research problems as discussed (in Chapter-8 and Chapter-9) along with sufficient allusions can enormously help the graduate researchers to identify topics of their own choice
The detailed case studies on climatological and hydrological time series prediction, covered throughout the monograph, are expected to grow interest in the BN-based prediction models and to further explore their potentiality to solve problems from similar domains
Erscheinungsjahr: | 2020 |
---|---|
Fachbereich: | Technik allgemein |
Genre: | Technik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Seiten: | 176 |
Reihe: | Studies in Computational Intelligence |
Inhalt: |
xxiii
149 S. 8 s/w Illustr. 59 farbige Illustr. 149 p. 67 illus. 59 illus. in color. |
ISBN-13: | 9783030277512 |
ISBN-10: | 3030277518 |
Sprache: | Englisch |
Ausstattung / Beilage: | Paperback |
Einband: | Kartoniert / Broschiert |
Autor: |
Ghosh, Soumya K.
Das, Monidipa |
Auflage: | 1st ed. 2020 |
Hersteller: |
Springer International Publishing
Springer International Publishing AG Studies in Computational Intelligence |
Maße: | 235 x 155 x 10 mm |
Von/Mit: | Soumya K. Ghosh (u. a.) |
Erscheinungsdatum: | 19.11.2020 |
Gewicht: | 0,277 kg |
This is the first text that throws light on the recent advancements in developing enhanced Bayesian network (BN) models to address the various challenges in spatial time series prediction
The monograph covers both theoretical and empirical aspects of a number of enhanced Bayesian network models, in a lucid, precise, and highly comprehensive manner
The monograph includes plenty of illustrative examples and proofs which will immensely help the reader to better understand the working principles of the enhanced BN models.
The open research problems as discussed (in Chapter-8 and Chapter-9) along with sufficient allusions can enormously help the graduate researchers to identify topics of their own choice
The detailed case studies on climatological and hydrological time series prediction, covered throughout the monograph, are expected to grow interest in the BN-based prediction models and to further explore their potentiality to solve problems from similar domains
Erscheinungsjahr: | 2020 |
---|---|
Fachbereich: | Technik allgemein |
Genre: | Technik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Seiten: | 176 |
Reihe: | Studies in Computational Intelligence |
Inhalt: |
xxiii
149 S. 8 s/w Illustr. 59 farbige Illustr. 149 p. 67 illus. 59 illus. in color. |
ISBN-13: | 9783030277512 |
ISBN-10: | 3030277518 |
Sprache: | Englisch |
Ausstattung / Beilage: | Paperback |
Einband: | Kartoniert / Broschiert |
Autor: |
Ghosh, Soumya K.
Das, Monidipa |
Auflage: | 1st ed. 2020 |
Hersteller: |
Springer International Publishing
Springer International Publishing AG Studies in Computational Intelligence |
Maße: | 235 x 155 x 10 mm |
Von/Mit: | Soumya K. Ghosh (u. a.) |
Erscheinungsdatum: | 19.11.2020 |
Gewicht: | 0,277 kg |