Pub Date : 2019-09-21DOI: 10.5772/intechopen.88650
Nur Iriawan, S. D. P. Yasmirullah
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.
{"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}
Pub Date : 2019-07-17DOI: 10.5772/INTECHOPEN.87994
D. Olayungbo
This study applies Bayesian graphical networks (BGN) using Bayesian graphical vector autoregressive (BGVAR) model with efficient Markov chain Monte Carlo (MCMC) Metropolis-Hastings (M-H) sampling algorithm in a dynamic interaction among mone- tary policies and macroeconomic performances in Nigeria for the period of 1986Q1 – 2017Q4. The motivation stems from the instability in the movement of exchange rate, inflation rate and interest rate in Nigeria over the past years as a result of the structure of the economy. In this way, the monetary authority periodically applies the various policy instruments to stabilize the economy using reserve and money supply as at when due. This study adapts VAR and SVAR structure to examine the dynamic interaction among variables of interest, using BN, to provide a better understanding of the monetary policy dynamics and fit the changing structure of the Nigeria ’ s economy as regards the dynamics in her economic structure. Our results show that inflation is the strong predictor of interest rate in Nigeria. A monetary policy of broad inflation targeting is recommended for the country.
{"title":"Bayesian Graphical Model Application for Monetary Policy and Macroeconomic Performance in Nigeria","authors":"D. Olayungbo","doi":"10.5772/INTECHOPEN.87994","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.87994","url":null,"abstract":"This study applies Bayesian graphical networks (BGN) using Bayesian graphical vector autoregressive (BGVAR) model with efficient Markov chain Monte Carlo (MCMC) Metropolis-Hastings (M-H) sampling algorithm in a dynamic interaction among mone- tary policies and macroeconomic performances in Nigeria for the period of 1986Q1 – 2017Q4. The motivation stems from the instability in the movement of exchange rate, inflation rate and interest rate in Nigeria over the past years as a result of the structure of the economy. In this way, the monetary authority periodically applies the various policy instruments to stabilize the economy using reserve and money supply as at when due. This study adapts VAR and SVAR structure to examine the dynamic interaction among variables of interest, using BN, to provide a better understanding of the monetary policy dynamics and fit the changing structure of the Nigeria ’ s economy as regards the dynamics in her economic structure. Our results show that inflation is the strong predictor of interest rate in Nigeria. A monetary policy of broad inflation targeting is recommended for the country.","PeriodicalId":317166,"journal":{"name":"Bayesian Networks - Advances and Novel Applications","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127978377","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}
Pub Date : 2019-06-05DOI: 10.5772/INTECHOPEN.80464
B. Zoullouti, M. Amghar, S. Nawal
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
{"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}
Pub Date : 2019-05-29DOI: 10.5772/INTECHOPEN.85976
O. Kupervasser
Previously, computational drag design was usually based on simplified laws of molecular physics, used for calculation of ligand ’ s interaction with an active site of a protein-enzyme. However, currently, this interaction is widely estimated using some statistical properties of known ligand-protein complex properties. Such statistical properties are described by quantitative structure-activity relationships (QSAR). Bayesian networks can help us to evaluate stability of a ligand-protein complex using found statistics. Moreover, we are possible to prove optimality of Naive Bayes model that makes these evaluations simple and easy for practical realization. We prove here optimality of Naive Bayes model using as an illustration ligand-protein interaction.
{"title":"Quantitative Structure-Activity Relationship Modeling and Bayesian Networks: Optimality of Naive Bayes Model","authors":"O. Kupervasser","doi":"10.5772/INTECHOPEN.85976","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.85976","url":null,"abstract":"Previously, computational drag design was usually based on simplified laws of molecular physics, used for calculation of ligand ’ s interaction with an active site of a protein-enzyme. However, currently, this interaction is widely estimated using some statistical properties of known ligand-protein complex properties. Such statistical properties are described by quantitative structure-activity relationships (QSAR). Bayesian networks can help us to evaluate stability of a ligand-protein complex using found statistics. Moreover, we are possible to prove optimality of Naive Bayes model that makes these evaluations simple and easy for practical realization. We prove here optimality of Naive Bayes model using as an illustration ligand-protein interaction.","PeriodicalId":317166,"journal":{"name":"Bayesian Networks - Advances and Novel Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134110633","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}
Pub Date : 2019-02-06DOI: 10.5772/INTECHOPEN.83607
D. S. McNair
As a child, I was raised as a Lutheran, with an earnest interest and concern for scripture. I became notorious for asking my Sunday school teachers imponderable and impolitic questions. Upon encountering Genesis 3:11–13 around age 6, I noticed that God confronts Adam in the Garden of Eden and asks, “Have you eaten from the tree?” Adam prevaricates: “The woman whom you gave to be with me, she gave me fruit from the tree.” God inquires of Eve about this. She answers, “The serpent tricked me.” My youngster mind recognized this pattern of dialog as very much akin to my own defensive dissembling with my parents when I had been the cause of some accident or had done something wrong. I very much wanted to know why Adam’s and Eve’s reasoning was insufficient.
{"title":"Introductory Chapter: Timeliness of Advantages of Bayesian Networks","authors":"D. S. McNair","doi":"10.5772/INTECHOPEN.83607","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.83607","url":null,"abstract":"As a child, I was raised as a Lutheran, with an earnest interest and concern for scripture. I became notorious for asking my Sunday school teachers imponderable and impolitic questions. Upon encountering Genesis 3:11–13 around age 6, I noticed that God confronts Adam in the Garden of Eden and asks, “Have you eaten from the tree?” Adam prevaricates: “The woman whom you gave to be with me, she gave me fruit from the tree.” God inquires of Eve about this. She answers, “The serpent tricked me.” My youngster mind recognized this pattern of dialog as very much akin to my own defensive dissembling with my parents when I had been the cause of some accident or had done something wrong. I very much wanted to know why Adam’s and Eve’s reasoning was insufficient.","PeriodicalId":317166,"journal":{"name":"Bayesian Networks - Advances and Novel Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114480188","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}
Pub Date : 2018-11-05DOI: 10.5772/INTECHOPEN.80064
L. Silva, João Bezerra, M. Perkusich, K. Gorgônio, H. Almeida, A. Perkusich
Bayesian networks can be built based on knowledge, data, or both. Independent of the source of information used to build the model, inaccuracies might occur or the application domain might change. Therefore, there is a need to continuously improve the model during its usage. As new data are collected, algorithms to continuously incorporate the updated knowledge can play an essential role in this process. In regard to the continu- ous learning of the Bayesian network’s structure, the current solutions are based on its structural refinement or adaptation. Recent researchers aim to reduce complexity and memory usage, allowing to solve complex and large-scale practical problems. This study aims to identify and evaluate solutions for the continuous learning of the Bayesian net- work’s structures, as well as to outline related future research directions. Our attention remains on the structures because the accurate parameters are completely useless if the structure is not representative.
{"title":"Continuous Learning of the Structure of Bayesian Networks: A Mapping Study","authors":"L. Silva, João Bezerra, M. Perkusich, K. Gorgônio, H. Almeida, A. Perkusich","doi":"10.5772/INTECHOPEN.80064","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.80064","url":null,"abstract":"Bayesian networks can be built based on knowledge, data, or both. Independent of the source of information used to build the model, inaccuracies might occur or the application domain might change. Therefore, there is a need to continuously improve the model during its usage. As new data are collected, algorithms to continuously incorporate the updated knowledge can play an essential role in this process. In regard to the continu- ous learning of the Bayesian network’s structure, the current solutions are based on its structural refinement or adaptation. Recent researchers aim to reduce complexity and memory usage, allowing to solve complex and large-scale practical problems. This study aims to identify and evaluate solutions for the continuous learning of the Bayesian net- work’s structures, as well as to outline related future research directions. Our attention remains on the structures because the accurate parameters are completely useless if the structure is not representative.","PeriodicalId":317166,"journal":{"name":"Bayesian Networks - Advances and Novel Applications","volume":"14 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":"117069709","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}
Pub Date : 2018-11-05DOI: 10.5772/INTECHOPEN.79916
R. A. Valdés, V. F. G. Comendador, A. Sanz, E. S. Ayra, J. A. P. Castán, L. P. Sanz
Additional information is available at the end of the chapter Abstract Most decisions in aviation regarding systems and operation are currently taken under uncertainty, relaying in limited measurable information, and with little assistance of formal methods and tools to help decision makers to cope with all those uncertainties. This chapter illustrates how Bayesian analysis can constitute a systematic approach for dealing with uncertainties in aviation and air transport. The chapter addresses the three main ways in which Bayesian networks are currently employed for scientific or regulatory decision-making purposes in the aviation industry, depending on the extent to which decision makers rely totally or partially on formal methods. These three alternatives are illustrated with three aviation case studies that reflect research work carried out by the authors.
{"title":"Bayesian Networks for Decision-Making and Causal Analysis under Uncertainty in Aviation","authors":"R. A. Valdés, V. F. G. Comendador, A. Sanz, E. S. Ayra, J. A. P. Castán, L. P. Sanz","doi":"10.5772/INTECHOPEN.79916","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.79916","url":null,"abstract":"Additional information is available at the end of the chapter Abstract Most decisions in aviation regarding systems and operation are currently taken under uncertainty, relaying in limited measurable information, and with little assistance of formal methods and tools to help decision makers to cope with all those uncertainties. This chapter illustrates how Bayesian analysis can constitute a systematic approach for dealing with uncertainties in aviation and air transport. The chapter addresses the three main ways in which Bayesian networks are currently employed for scientific or regulatory decision-making purposes in the aviation industry, depending on the extent to which decision makers rely totally or partially on formal methods. These three alternatives are illustrated with three aviation case studies that reflect research work carried out by the authors.","PeriodicalId":317166,"journal":{"name":"Bayesian Networks - Advances and Novel Applications","volume":"311 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":"122822600","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}
Pub Date : 2018-11-05DOI: 10.5772/INTECHOPEN.81111
D. Faria, C. Premebida, Luis J. Manso, Eduardo Parente Ribeiro, P. Núñez
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.
{"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}
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