Bayesian Networks - Advances and Novel Applications最新文献

An Economic Growth Model Using Hierarchical Bayesian Method 基于层次贝叶斯方法的经济增长模型

Bayesian Networks - Advances and Novel Applications Pub Date : 2019-09-21 DOI: 10.5772/intechopen.88650

Nur Iriawan, S. D. P. Yasmirullah

{"title":"An Economic Growth Model Using Hierarchical Bayesian Method","authors":"Nur Iriawan, S. D. P. Yasmirullah","doi":"10.5772/intechopen.88650","DOIUrl":"https://doi.org/10.5772/intechopen.88650","url":null,"abstract":"Economic growth can be used as an assessment for the success of the regional economic establishment. Since the Regulation of the Republic Indonesia Number 32 of 2004 has been implemented, the imbalance economic growth among the regencies in Indonesia is rising. The imbalance in the conditions of economic growth differs between regions with the aim of the government to improve social welfare by expanding economic activities in each region. The purpose of this chapter is to elaborate whether there is a difference in economic growth based on the distribution of bank credit for each regency in Indonesia. This research analyzes the economic growth data using hierarchical structure model that follows the normality-based modeling in the first level. The two modeling approaches will be applied, i.e., a general one-level Bayesian approach and a two-level structure hierarchical Bayesian approach. The success of these approaches has demonstrated that the two-level hierarchical structure Bayesian has a better estimation than a general one-level Bayesian. It demonstrates that all of the macro-level characteristics of provinces are significantly influencing the different economic growth in every related province. These variations are also significantly influenced by their cross-level interaction regency and provincial characteristics.","PeriodicalId":317166,"journal":{"name":"Bayesian Networks - Advances and Novel Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133406830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Bayesian Graphical Model Application for Monetary Policy and Macroeconomic Performance in Nigeria 贝叶斯图形模型在尼日利亚货币政策和宏观经济绩效中的应用

Bayesian Networks - Advances and Novel Applications Pub Date : 2019-07-17 DOI: 10.5772/INTECHOPEN.87994

D. Olayungbo

引用次数: 0

Using Bayesian Networks for Risk Assessment in Healthcare System 基于贝叶斯网络的医疗系统风险评估

Bayesian Networks - Advances and Novel Applications Pub Date : 2019-06-05 DOI: 10.5772/INTECHOPEN.80464

B. Zoullouti, M. Amghar, S. Nawal

{"title":"Using Bayesian Networks for Risk Assessment in Healthcare System","authors":"B. Zoullouti, M. Amghar, S. Nawal","doi":"10.5772/INTECHOPEN.80464","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.80464","url":null,"abstract":"To ensure patient safety, the healthcare service must be of a high quality, safe and effective. This work aims to propose integrated approaches to risk management for a hospital system. To improve patient ’ s safety, we should develop methods where different aspects of risk and type of information are taken into consideration. The first approach is designed for a context where data about risk events are available. It uses Bayesian networks for quantitative risk analysis in the hospital. Bayesian networks provide a framework for presenting causal relationships and enable probabilistic inference among a set of variables. The methodology is used to analyze the patient ’ s safety risk in the operating room, which is a high risk area for adverse event. The second approach uses the fuzzy Bayesian network to model and analyze risk. Fuzzy logic allows using the expert ’ s opinions when quantitative data are lacking and only qualitative or vague statements can be made. This approach provides an actionable model that accurately supports human cognition using linguistic variables. A case study of the patient ’ s safety risk in the operating room is used to illustrate the application of the proposed method. s","PeriodicalId":317166,"journal":{"name":"Bayesian Networks - Advances and Novel Applications","volume":"169 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120871048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Quantitative Structure-Activity Relationship Modeling and Bayesian Networks: Optimality of Naive Bayes Model 定量构效关系建模与贝叶斯网络:朴素贝叶斯模型的最优性

Bayesian Networks - Advances and Novel Applications Pub Date : 2019-05-29 DOI: 10.5772/INTECHOPEN.85976

O. Kupervasser

引用次数: 2

Introductory Chapter: Timeliness of Advantages of Bayesian Networks 导论章:贝叶斯网络优势的时效性

Bayesian Networks - Advances and Novel Applications Pub Date : 2019-02-06 DOI: 10.5772/INTECHOPEN.83607

D. S. McNair

引用次数: 1

Continuous Learning of the Structure of Bayesian Networks: A Mapping Study 贝叶斯网络结构的持续学习:一种映射研究

Bayesian Networks - Advances and Novel Applications Pub Date : 2018-11-05 DOI: 10.5772/INTECHOPEN.80064

L. Silva, João Bezerra, M. Perkusich, K. Gorgônio, H. Almeida, A. Perkusich

引用次数: 5

Bayesian Networks for Decision-Making and Causal Analysis under Uncertainty in Aviation 航空不确定性决策与因果分析的贝叶斯网络

Bayesian Networks - Advances and Novel Applications Pub Date : 2018-11-05 DOI: 10.5772/INTECHOPEN.79916

R. A. Valdés, V. F. G. Comendador, A. Sanz, E. S. Ayra, J. A. P. Castán, L. P. Sanz

引用次数: 4

Multimodal Bayesian Network for Artificial Perception 人工感知的多模态贝叶斯网络

Bayesian Networks - Advances and Novel Applications Pub Date : 2018-11-05 DOI: 10.5772/INTECHOPEN.81111

D. Faria, C. Premebida, Luis J. Manso, Eduardo Parente Ribeiro, P. Núñez

{"title":"Multimodal Bayesian Network for Artificial Perception","authors":"D. Faria, C. Premebida, Luis J. Manso, Eduardo Parente Ribeiro, P. Núñez","doi":"10.5772/INTECHOPEN.81111","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.81111","url":null,"abstract":"In order to make machines perceive their external environment coherently, multiple sources of sensory information derived from several different modalities can be used (e.g. cameras, LIDAR, stereo, RGB-D, and radars). All these different sources of information can be efficiently merged to form a robust perception of the environment. Some of the mechanisms that underlie this merging of the sensor information are highlighted in this chapter, showing that depending on the type of information, different combination and integration strategies can be used and that prior knowledge are often required for interpreting the sensory signals efficiently. The notion that perception involves Bayesian inference is an increasingly popular position taken by a considerable number of researchers. Bayesian models have provided insights into many perceptual phenomena, showing that they are a valid approach to deal with real-world uncertainties and for robust classification, including classification in time-dependent problems. This chapter addresses the use of Bayesian networks applied to sensory perception in the following areas: mobile robotics, autonomous driving systems, advanced driver assistance systems, sensor fusion for object detection, and EEG-based mental states classification.","PeriodicalId":317166,"journal":{"name":"Bayesian Networks - Advances and Novel Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130637763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0