| 书目名称 | Enhanced Bayesian Network Models for Spatial Time Series Prediction |
| 副标题 | Recent Research Tren |
| 编辑 | Monidipa Das,Soumya K. Ghosh |
| 视频video | |
| 概述 | 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 monograp |
| 丛书名称 | Studies in Computational Intelligence |
| 图书封面 |  |
| 描述 | This 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 |
| 出版日期 | Book 2020 |
| 关键词 | Spatio-temporal data; Spatial time series prediction; Applied machine learning; Computational Intellige |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-27749-9 |
| isbn_softcover | 978-3-030-27751-2 |
| isbn_ebook | 978-3-030-27749-9Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer Nature Switzerland AG 2020 |
发表于 2025-3-21 18:56:36
