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Construction of Monitoring Model and Algorithm Design on Passenger Security during Shipping Based on Improved Bayesian Network

Jiali Wang, Qingnian Zhang, Wenfeng Ji
2014 The Scientific World Journal  
The security of passengers during shipping is affected by various factors, and it is hard to predict and control.  ...  The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained.  ...  Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper.  ... 
doi:10.1155/2014/158652 pmid:25254227 pmcid:PMC4164846 fatcat:fxtenqd6czguffvwmrkqrnkgh4

Modelling gross margins and potential N exports from cropland in south-eastern Australia

David Nash, Penny Riffkin, Robert Harris, Alan Blackburn, Cam Nicholson, Mark McDonald
2013 European Journal of Agronomy  
The Agricultural Productions Systems Simulator (APSIM) was used to model crop yields for which gross margins were estimated and a Bayesian Network used to estimate environmental risk.  ...  This is counter intuitive as it implies N fertiliser applications can lessen N exports.  ...  Industries, Gerard Bibby of GrainCorp Operations Limited and Dr Ann Nicholson from Bayesian Intelligence Pty.  ... 
doi:10.1016/j.eja.2013.01.001 fatcat:bjy45q3vfbenfnd4ghguxn3ulq

The Dutch's Real World Financial Institute: Introducing Quantum-Like Bayesian Networks as an Alternative Model to deal with Uncertainty [article]

Catarina Moreira and Emmanuel Haven and Sandro Sozzo and Andreas Wichert
2017 arXiv   pre-print
Experimental results attest the efficiency of the quantum-like Bayesian networks, since the application of interference terms is able to reduce the error percentage of inferences performed over quantum-like  ...  The goal is to work out a decision model, which represents the underlying tasks that make up the loan application service, and to assess potential areas of improvement of the institution's internal processes  ...  explore the applicability and effectiveness of Quantum-Like Bayesian Networks [31] in the prediction of several events from the loan application process.  ... 
arXiv:1710.00490v1 fatcat:43oqvcwkqngxdm4swzqtka2bvu

Process mining with real world financial loan applications: Improving inference on incomplete event logs

Catarina Moreira, Emmanuel Haven, Sandro Sozzo, Andreas Wichert, Fenghua Wen
2018 PLoS ONE  
The absence of data is translated into a drastic decrease of precision and compromises the decision models, leading to biased and unrepresentative results.  ...  In this work, we analyse and model a real life financial loan application belonging to a sample bank in the Netherlands.  ...  Advantages and disadvantages of quantum-like Bayesian networks It is straightforward that quantum-like Bayesian networks suffer the same problem of the exponential increase of complexity (expressed as  ... 
doi:10.1371/journal.pone.0207806 pmid:30596655 pmcid:PMC6312323 fatcat:loqyvaalezez5im3v73q6wibqq

Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities

Sumeet Gupta, Hee W. Kim
2008 European Journal of Operational Research  
Bayesian networks are limited in differentiating between causal and spurious relationships among decision factors. Decision making without differentiating the two relationships cannot be effective.  ...  To overcome this limitation of Bayesian networks, this study proposes linking Bayesian networks to structural equation modeling (SEM), which has an advantage in testing causal relationships between factors  ...  Second, it is related to the application of operations research, as it examines how to make decisions for retaining customers in a virtual community based on the applications of SEM and Bayesian networks  ... 
doi:10.1016/j.ejor.2007.05.054 fatcat:7gm42dbi5vdyrovpzluuyil7ju

Locating Anomalies Using Bayesian Factorizations and Masks

Li Yao, Amaury Lendasse, Francesco Corona
2011 The European Symposium on Artificial Neural Networks  
Masks are geometrically generated based on the factorization of the joint probability from a Bayesian network automatically learnt from the given data set.  ...  A plethora of methods have been developed to handle anomaly detection in various application domains.  ...  Factorization with Bayesian networks Bayesian networks exploit the conditional independence within a joint distribution and the use of a Directed Acyclic Graph (DAG) allows a compact representation of  ... 
dblp:conf/esann/YaoLC11 fatcat:o27xtrvrcbaq7fm6ql6dayolga

An Intuitive Dashboard for Bayesian Network Inference

Vikas Reddy, Anna Charisse Farr, Paul Wu, Kerrie Mengersen, Prasad K D V Yarlagadda
2014 Journal of Physics, Conference Series  
Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications.  ...  However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large  ...  Acknowledgments The Airports of the Future research project is supported under the Australian Research Councils Linkage Projects funding scheme (LP0990135).  ... 
doi:10.1088/1742-6596/490/1/012023 fatcat:zssct3ye2bbcve4usvbamkjm3a

A review on probabilistic graphical models in evolutionary computation

Pedro Larrañaga, Hossein Karshenas, Concha Bielza, Roberto Santana
2012 Journal of Heuristics  
Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains.  ...  Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these  ...  Figure 2 shows an exemplary Markov network structure and the parameters for one of its factors.  ... 
doi:10.1007/s10732-012-9208-4 fatcat:54ipbzsryfbt5nqmaczgurb2he

A causal mapping approach to constructing Bayesian networks

Sucheta Nadkarni, Prakash P. Shenoy
2004 Decision Support Systems  
In Section 5, we describe a procedure for constructing a causal map and its conversion to a Bayesian network.  ...  Bayesian Networks In this section, we briefly describe the definition and semantics of Bayesian networks.  ...  Acknowledgements The research reported in this paper has been supported by two grants from the Kansas University  ... 
doi:10.1016/s0167-9236(03)00095-2 fatcat:r5lhnuuzezhljbje74v7qnvhvq

Hardware Accelerator for Probabilistic Inference in 65-nm CMOS

Osama U. Khan, David D. Wentzloff
2016 IEEE Transactions on Very Large Scale Integration (vlsi) Systems  
The ALARM Bayesian network is used to benchmark the performance of the accelerator.  ...  A hardware accelerator is presented to compute the probabilistic inference for a Bayesian Network (BN) in distributed sensing applications.  ...  It is known that the complexity of a Bayesian Network (BN) grows exponentially with the number of nodes in the network [15] .  ... 
doi:10.1109/tvlsi.2015.2420663 fatcat:hv6vl2hwnzberitviwsgmspjzq

Probabilistic Reasoning in Bayesian Networks: A Relational Database Approach [chapter]

S. K. Michael Wong, Dan Wu, Cory J. Butz
2003 Lecture Notes in Computer Science  
We adapt a method for answering queries in database theory to the setting of probabilistic reasoning in Bayesian networks.  ...  Probabilistic reasoning in Bayesian networks is normally conducted on a junction tree by repeatedly applying the local propagation whenever new evidence is observed.  ...  i)and its Markov factorization as follows:p(abcd) = p(ab)·p(ac)·p(ad) p(a)·p(a) In other words, the marginal p(h ′ i ) can be computed from the original marginal p(h) supplied with the Markov network  ... 
doi:10.1007/3-540-44886-1_54 fatcat:dnld6soryrb3hl4vzw4cp3puha

Forward Amortized Inference for Likelihood-Free Variational Marginalization [article]

Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya, Eva W. P. van den Borne, Yağmur Güçlütürk, Max Hinne, Eric Maris, Marcel A. J. van Gerven
2018 arXiv   pre-print
The resulting forward amortized variational inference is a likelihood-free method as its gradient can be sampled without bias and without requiring any evaluation of either the model joint distribution  ...  In the second example we train an amortized variational approximation of a Bayesian optimal classifier by marginalizing over the model space.  ...  The total and marginalized generative models for the Bayesian meta-classifier. B.  ... 
arXiv:1805.11542v1 fatcat:4bpeolqy6vb57eejgac2afb4ya

Bayesian Networks for the Evaluation of Complex Systems Availability

El Hassen Ait Mokhtar, Radouane Kara, Alaa Chateauneuf
2014 International Workshop on Verification and Evaluation of Computer and Communication Systems  
In fact, they permit the modelling of systems configured as network and the computation of marginal probabilities of the nodes of the system using prior and conditional probabilities.  ...  the different links and factors that can affect the availability and reliability of such systems.  ...  BAYESIAN INFERENCE Bayesian networks are essentially used to compute the marginal and posterior probabilities of events connected between each other by relations of cause and effect.  ... 
dblp:conf/vecos/MokhtarKC14 fatcat:laojpc3f5vfqxpqhvwhhcqx55i

Use of Bayesian networks to dissect the complexity of genetic disease: application to the Genetic Analysis Workshop 17 simulated data

Jia Kang, Wei Zheng, Lun Li, Joon Lee, Xiting Yan, Hongyu Zhao
2011 BMC Proceedings  
We use the Bayesian network as a tool for the risk prediction of disease outcome.  ...  To better understand the mechanism of disease onset, it is of great interest to systematically investigate the crosstalk among various risk factors.  ...  Acknowledgments Thanks are due to the Yale University Biomedical High Performance Computing Center and the National Institutes of Health (NIH) grant (RR19895) that funded the instrumentation.  ... 
doi:10.1186/1753-6561-5-s9-s37 pmid:22373110 pmcid:PMC3287873 fatcat:2rgdrss46jeoze5k3krpsc4o7y

Modeling and Change Detection of Dynamic Network Data by a Network State Space Model

Na Zou, Jing Li
2016 Figshare  
in the form of networks.  ...  There are two types of variability in dynamic network data: variability of natural evolution and variability due to assignable causes. The latter is the "change" referred to in this paper.  ...  It is applicable when the true posterior distribution takes the form of a factor product, i.e., ( | ) = 1 ( ) ∏ ( ) =0 .  ... 
doi:10.6084/m9.figshare.3443399.v1 fatcat:tevgxhhxfza7fdnlyutjsirpbq
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