標(biāo)題: Titlebook: Enhanced Bayesian Network Models for Spatial Time Series Prediction; Recent Research Tren Monidipa Das,Soumya K. Ghosh Book 2020 Springer N [打印本頁] 作者: Abridge 時(shí)間: 2025-3-21 18:56
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
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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
作者: notification 時(shí)間: 2025-3-21 20:42
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.作者: 注意 時(shí)間: 2025-3-22 02:49 作者: 獎(jiǎng)牌 時(shí)間: 2025-3-22 05:04 作者: nepotism 時(shí)間: 2025-3-22 12:18 作者: oxidant 時(shí)間: 2025-3-22 13:10 作者: oxidant 時(shí)間: 2025-3-22 18:00 作者: commodity 時(shí)間: 2025-3-22 23:12 作者: employor 時(shí)間: 2025-3-23 04:35 作者: RALES 時(shí)間: 2025-3-23 07:08
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 作者: Feedback 時(shí)間: 2025-3-23 10:57
https://doi.org/10.1007/978-3-319-65256-6mes large, containing several nodes and edges, the .?of Bayesian network (BN) analysis increases at a large extent. Now, in many cases of .?prediction, it is necessary to take into account the influences of variables from large number of spatially distributed locations, which becomes almost intracta作者: 小歌劇 時(shí)間: 2025-3-23 14:56
Fabien Escalona,Daniel Keith,Luke Marchof available training dataset. A proper learning of the network needs large amount of observed data be available during the training procedure. Otherwise, it may result in strongly biased inference due to .. The recent research indicates that a prior knowledge about the respective . ?may help in red作者: 發(fā)電機(jī) 時(shí)間: 2025-3-23 20:21 作者: 規(guī)章 時(shí)間: 2025-3-23 23:35 作者: tenuous 時(shí)間: 2025-3-24 06:06 作者: 無政府主義者 時(shí)間: 2025-3-24 09:39 作者: VOC 時(shí)間: 2025-3-24 13:01
,Spatial Time Series Prediction Using Advanced BN Models—An Application Perspective,on the synergism of enhanced BN models to handle more complex ST prediction scenarios in real life. We anticipate that the chapter will help researchers to find out several interesting research issues yet to be resolved and will also encourage them to further explore the intrinsic power of BNs to tackle the same.作者: 追逐 時(shí)間: 2025-3-24 18:36 作者: 賭博 時(shí)間: 2025-3-24 21:27
parameter learning and .. We also cover the basic concepts of various categories of .?, including dynamic Bayesian network, fuzzy Bayesian network, spatial Bayesian network, semantic Bayesian network etc. Further, we discuss on the potentials of BN in modeling the inter-variable dependencies while analyzing ..作者: 蔓藤圖飾 時(shí)間: 2025-3-24 23:35 作者: 欺騙手段 時(shí)間: 2025-3-25 05:45
on the synergism of enhanced BN models to handle more complex ST prediction scenarios in real life. We anticipate that the chapter will help researchers to find out several interesting research issues yet to be resolved and will also encourage them to further explore the intrinsic power of BNs to tackle the same.作者: 憤怒歷史 時(shí)間: 2025-3-25 08:16
Book 2020results 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 Ba作者: UTTER 時(shí)間: 2025-3-25 13:42
ue to the difficulty faced by research beginners to get a unified view of evolution of the relevant research from the scattered literature and eventually this is identified as the source of motivation behind this monograph. Finally, the chapter ends with a section outlining the overall structure of the remainder of the monograph.作者: hypnotic 時(shí)間: 2025-3-25 18:58 作者: 聲明 時(shí)間: 2025-3-25 23:30
Fabien Escalona,Daniel Keith,Luke Marchbeen evaluated in comparison with a number of conventional statistical and state-of-the-art space-time prediction models, with respect to a case study on climatological .. Experimental result demonstrates the superiority of semBnet over the other models considered.作者: Myofibrils 時(shí)間: 2025-3-26 03:09 作者: ADAGE 時(shí)間: 2025-3-26 04:57
Bayesian Network with Residual Correction Mechanism,dels, with respect to case studies on climatological and hydrological . . Experimental result demonstrates effectiveness of BNRC in spatial time series prediction under the paucity of influencing variables.作者: modifier 時(shí)間: 2025-3-26 12:06
Semantic Bayesian Network,been evaluated in comparison with a number of conventional statistical and state-of-the-art space-time prediction models, with respect to a case study on climatological .. Experimental result demonstrates the superiority of semBnet over the other models considered.作者: 可行 時(shí)間: 2025-3-26 12:36 作者: 旅行路線 時(shí)間: 2025-3-26 18:16
https://doi.org/10.1007/978-3-031-35151-8at, even with the extended functionality, the parameter learning complexities in the enhanced BN models do not increase considerably compared to the standard BN model for spatial time series prediction.作者: 含水層 時(shí)間: 2025-3-27 00:12 作者: 其他 時(shí)間: 2025-3-27 03:08 作者: 小鹿 時(shí)間: 2025-3-27 07:03 作者: 身體萌芽 時(shí)間: 2025-3-27 09:30 作者: Oversee 時(shí)間: 2025-3-27 16:47 作者: monochromatic 時(shí)間: 2025-3-27 18:43 作者: TOXIN 時(shí)間: 2025-3-28 01:49
Semantic Bayesian Network,of available training dataset. A proper learning of the network needs large amount of observed data be available during the training procedure. Otherwise, it may result in strongly biased inference due to .. The recent research indicates that a prior knowledge about the respective . ?may help in red作者: RECUR 時(shí)間: 2025-3-28 02:11 作者: 感激小女 時(shí)間: 2025-3-28 06:15
Comparative Study of Parameter Learning Complexities of Enhanced Bayesian Networks,formed from both the perspectives of .?and .??requirement. The chapter starts with a description of a common .?, specifying the total number of nodes/variables, maximum number of parents for any node in the network, maximum domain size of the variables, total number of spatial locations etc. Later, 作者: 俗艷 時(shí)間: 2025-3-28 12:51
,Spatial Time Series Prediction Using Advanced BN Models—An Application Perspective,ls from the perspective of various applications of .?prediction. Eight different application domains including medical imaging, .?, transportation, bio-informatics, homeland security, environment/ecology, finance/economy, etc. have been considered for this purpose. Later, the chapter also discusses 作者: STELL 時(shí)間: 2025-3-28 18:22 作者: MUTED 時(shí)間: 2025-3-28 18:51 作者: BRIBE 時(shí)間: 2025-3-28 23:01
Book 2020fy 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作者: encyclopedia 時(shí)間: 2025-3-29 03:45 作者: aspect 時(shí)間: 2025-3-29 09:59
