找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Enhanced Bayesian Network Models for Spatial Time Series Prediction; Recent Research Tren Monidipa Das,Soumya K. Ghosh Book 2020 Springer N

[復(fù)制鏈接]
查看: 9891|回復(fù): 44
樓主
發(fā)表于 2025-3-21 18:56:36 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Enhanced Bayesian Network Models for Spatial Time Series Prediction
副標(biāo)題Recent Research Tren
編輯Monidipa Das,Soumya K. Ghosh
視頻videohttp://file.papertrans.cn/312/311246/311246.mp4
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
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Enhanced Bayesian Network Models for Spatial Time Series Prediction; Recent Research Tren Monidipa Das,Soumya K. Ghosh Book 2020 Springer N
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
關(guān)鍵詞Spatio-temporal data; Spatial time series prediction; Applied machine learning; Computational Intellige
版次1
doihttps://doi.org/10.1007/978-3-030-27749-9
isbn_softcover978-3-030-27751-2
isbn_ebook978-3-030-27749-9Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

書目名稱Enhanced Bayesian Network Models for Spatial Time Series Prediction影響因子(影響力)




書目名稱Enhanced Bayesian Network Models for Spatial Time Series Prediction影響因子(影響力)學(xué)科排名




書目名稱Enhanced Bayesian Network Models for Spatial Time Series Prediction網(wǎng)絡(luò)公開度




書目名稱Enhanced Bayesian Network Models for Spatial Time Series Prediction網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Enhanced Bayesian Network Models for Spatial Time Series Prediction被引頻次




書目名稱Enhanced Bayesian Network Models for Spatial Time Series Prediction被引頻次學(xué)科排名




書目名稱Enhanced Bayesian Network Models for Spatial Time Series Prediction年度引用




書目名稱Enhanced Bayesian Network Models for Spatial Time Series Prediction年度引用學(xué)科排名




書目名稱Enhanced Bayesian Network Models for Spatial Time Series Prediction讀者反饋




書目名稱Enhanced Bayesian Network Models for Spatial Time Series Prediction讀者反饋學(xué)科排名




單選投票, 共有 0 人參與投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用戶組沒有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 20:42:26 | 只看該作者
Summary and Future Research,actical issues in .?and the application of enhanced BN models to address the respective challenges. This chapter summarizes the various topics discussed in the present monograph and also puts forward a number of future research directions which have enormous opportunities to further explore BN models for spatial time series prediction.
板凳
發(fā)表于 2025-3-22 02:49:45 | 只看該作者
地板
發(fā)表于 2025-3-22 05:04:21 | 只看該作者
5#
發(fā)表于 2025-3-22 12:18:17 | 只看該作者
6#
發(fā)表于 2025-3-22 13:10:59 | 只看該作者
7#
發(fā)表于 2025-3-22 18:00:16 | 只看該作者
8#
發(fā)表于 2025-3-22 23:12:26 | 只看該作者
9#
發(fā)表于 2025-3-23 04:35:12 | 只看該作者
10#
發(fā)表于 2025-3-23 07:08:11 | 只看該作者
https://doi.org/10.1057/9781137317803 not always known properly which variable influences which other. In that case, modeling of spatio-temporal . using . (like Bayesian network) becomes a challenging task due to the lack of appropriate influencing nodes in the . . In this chapter, we introduce a novel architecture of . . ?(BNRC). The
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-11 01:43
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
独山县| 龙里县| 金华市| 博客| 双流县| 靖远县| 晋城| 竹山县| 西藏| 宁德市| 苍梧县| 蓬溪县| 当雄县| 健康| 柞水县| 潞西市| 类乌齐县| 呼玛县| 泾川县| 荥阳市| 大庆市| 云南省| 红桥区| 工布江达县| 乌兰县| 盐城市| 西峡县| 枝江市| 达拉特旗| 克山县| 元江| 太仓市| 吉安市| 渝中区| 新竹县| 舒兰市| 惠来县| 咸丰县| 台江县| 汶川县| 双流县|