Bayesian belief network (BBN)
Dynamic Bayesian belief network to model the development of walking and cycling schemes
... This paper aims to describe a model which represents the formulation of decision making processes (over a number of years) affecting the step-changes of walking and cycling (WaC) schemes. These processes can be seen as ...
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A Bayesian Belief Network for Murray Valley encephalitis virus risk assessment in Western Australia
... a Bayesian Belief Network (BBN), incorporating a range of abiotic, biotic and anthropo- genic factors that might affect features such as the pop- ulation densities of Ciconiiformes and ...
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Capturing Ecosystem Services, Stakeholders' Preferences and Trade-Offs in Coastal Aquaculture Decisions : A Bayesian Belief Network Application
... a Bayesian belief network (BBN) as a decision support system for mediating trade-offs between economic development, protection of natural ecosystems and coastal livelihoods, piloted in the case of ...
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Examination of Bayesian belief network for safety assessment of nuclear computer-based systems
... Abstract We report here on a continuation of work on the Bayesian Belief Network (BBN) model described in [Fenton, Littlewood et al. 1998]. As explained in the previous deliver- able, our model ...
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Knowledge Discovery for Query Formulation for Validation of a Bayesian Belief Network
... techniques. Bayesian belief networks (BBN) have proven to be computationally viable empirical probabilistic models of data ...of Bayesian belief networks, particularly for classification and ...
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Application of multiple linear regression and Bayesian belief network approaches to model life risk to beach users in the UK
... Where data are available, as in the case of this problem, a range of algorithms are available to support structure learning. These algorithms fall into three broad categories: constraint-based, score-based, and hybrid ...
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Bayesian Belief Network, Bayesian Learning, Information Security, Intelligent Agent, Risk Assessment.
... the Bayesian Belief Network is the choice because it could graphically represent the probabilistic relationships regarding to the data set which we ...the network and the probability ...
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Online Decision Support System and Machine Learning Modeling using Bayesian Belief Network
... of Bayesian Belief Networks (BBN) for developing a practical framework for machine learning process incorporating the commonsense ...reasoning. Bayesian Belief Networks grant a systematic and ...
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Elicitation of Bayesian Belief Network (EBBN) using Z-Number Approach
... Bayesian Network (BN) is established in a wide variety of applications to provide cause-effect relationships of variables in a compact ...of Bayesian Belief Network ...diagnostic ...
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Bayesian belief network model for the safety assessment of nuclear computer-based systems
... We do not intend here to describe the theory of BBN models itself—there is an extensive existing literature on this (see e.g. [Pearl 1993, Pearl 1988, CACM 1995, Jensen 1996, Lauritzen and Spiegelhalter 1988]). We intend ...
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Use of Bayesian Belief Network techniques to explore the interaction of biosecurity practices on the probability of porcine disease occurrence in Canada
... identify and quantify the probability of disease occurrence in Canadian swine farms. A BBN is a probabilistic graphical model which represents a network of nodes connected by directed links that represent a ...
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Supply chain risk network management : a bayesian belief network and expected utility based approach for managing supply chain risks
... Leerojanaprapa et al. (2013) and Leerojanaprapa (2014) proposed a generic BBN modelling pro- cess to support supply chain risk analysis based on expert knowledge and conducted a case study in the medical supply chain to ...
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UK National Ecosystem Assessment Follow on Work package 7: Operationalising scenarios in the UK National Ecosystem Assessment Follow on
... The analysis of the MENE data was initially undertaken using GIS techniques and spread sheet analysis of the spatially referenced subset of the MENE data. While these approaches have been useful in exploring the ...
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Assessing Chemical Mixtures and Human Health: Use of Bayesian Belief Net Analysis
... Methods: Bayesian Belief Network (BBN) models were developed and empirically assessed in a cohort comprising 84 women aged 18 - 40 years who underwent a laparoscopy or laparotomy between 1999 and ...
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Bayesian belief networks for fault detection and diagnostics of a three phase separator
... A fault detection and diagnostic (FDD) method- ology based on Bayesian Belief Network (BBN) technique for TPS is proposed in this paper. The BBNs are used for modelling systems usually con- sisting ...
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A Survey on MR MNBC:MAX REL based Feature Selection for the Multi Relational Bayesian Belief Network
... A Bayesian network (BN)[9] consists of a directed, acyclic graph and a probability distribution for each node in that graph given its immediate ...Bayes Network Classifier is based on a ...
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A fuzzy based Bayesian Belief Network approach for railway bridge condition monitoring and fault detection
... example, Network Rail, which is the owner of the railway network in the UK, estimates to spend 35 billion of pounds over a 5-year period for mainte- nance and renewal activities of the railway ...
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Interdependency modeling of supply chain risks incorporating game theoretic risks
... Abstract – Most of the current risk quantification techniques being applied in the field of Supply Chain Risk Management consider risk factors to be independent. This research considers risks as interdependent triggers, ...
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Development of a clinical decision model for thyroid nodules
... information. Bayesian networks allow clinicians to derive insights about the data domain because the networks are graphical, hierarchical representations of how conditionally independent varia- bles associate to ...
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Mobile sensor network noise reduction and recalibration using a Bayesian network
... a Bayesian network based system, we are able to recover the erroneous readings and recalibrate the drifted sensors ...state-of-art Bayesian belief network techniques by incorporating ...
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