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Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks

Cheol Young Park, Kathryn Blackmond Laskey, Paulo C. G. Costa, Shou Matsumoto
2019 Applied Sciences  
Inference in CG networks can be NP-hard even for special-case structures, such as poly-trees, where inference in discrete Bayesian networks can be performed in polynomial time.  ...  Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., image understanding, data fusion, medical diagnosis, fraud detection  ...  Table 1 . 1 Possible Node Types in a Hybrid Bayesian Network (BN).  ... 
doi:10.3390/app9102055 fatcat:c3vocssyxvb6nerw3itdbc624m

Almost instant time inference for hybrid partially dynamic Bayesian networks

K. Chang
2007 IEEE Transactions on Aerospace and Electronic Systems  
or impossible for general hybrid networks.  ...  A Bayesian network (BN) is a compact representation for probabilistic models and inference. They have been used successfully for many military and civilian applications.  ...  We then present a computationally efficient method, the AIA for general hybrid Bayesian net inference.  ... 
doi:10.1109/taes.2007.357151 fatcat:tj5b2da3vbhn3a2gtzsnmvcmtm

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method

Bin Yu, Jia-Meng Xu, Shan Li, Cheng Chen, Rui-Xin Chen, Lei Wang, Yan Zhang, Ming-Hui Wang
2017 OncoTarget  
In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed  ...  Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature.  ...  network and we proposed a novel hybrid learning algorithm (DBNCS) based on DBN to construct the multiple time-delayed GRNs.  ... 
doi:10.18632/oncotarget.21268 pmid:29113310 pmcid:PMC5655205 fatcat:un3rdkise5gxbksbsi5th2vm6m

A Bayesian Network Hybrid Model for Representing Accident and Emergency Waiting Times

Adele H. Marshall, Louise Burns
2007 Computer-Based Medical Systems (CBMS), Proceedings of the IEEE Symposium on  
The technique uses Bayesian networks to capture the heterogeneity of arriving patients by representing how patient covariates interact to influence their waiting times in the department.  ...  As a result, activity targets are now established based on the patient total waiting times with much emphasis on trolley waits.  ...  BAYESIAN NETWORK HYBRID MODEL The Bayesian network hybrid model consists of two components as illustrated in Fig. 1 .  ... 
doi:10.1109/cbms.2007.1 dblp:conf/cbms/MarshallB07 fatcat:yuzolzshsvfpvdfiax5ey5mcqy

Hybrid Bayesian Network Models to Investigate the Impact of Built Environment Experience before Adulthood on Students' Tolerable Travel Time to Campus: Towards Sustainable Commute Behavior

Yu Chen, Mahdi Aghaabbasi, Mujahid Ali, Sergey Anciferov, Linar Sabitov, Sergey Chebotarev, Karina Nabiullina, Evgeny Sychev, Roman Fediuk, Rosilawati Zainol
2021 Sustainability  
The Bayesian network models were hybridized with the Pearson chi-square test to select the most relevant variables to predict the tolerable travel time. Two predictive models were developed.  ...  This present study developed two predictive and associative Bayesian network models to forecast the tolerable travel time of university students to campus.  ...  Bayesian Network Model BN is a probabilistic network model that employs the probability theory and the graph system concurrently. The theory behind the BN analysis is the Bayesian probability.  ... 
doi:10.3390/su14010325 fatcat:uj3far425jafhghqhhvlvywtpm

Modeling Traffic Information using Bayesian Networks

W.P. van den Haak, Rothkrantz L.J.M, P. Wiggers, B.M.R. Heijligers, T. Bakri, D. Vukovic
2010 Transactions on Transport Sciences  
This prediction model based only on historical data and our Bayesian Network are combined in a hybrid model, where we evaluate performance as well.  ...  KEY WORDS: Bayesian Networks, prediction, vehicle speed, inductive loop detector data.  ...  The Bayesian Network is trained for different time horizons, and evaluated for the corresponding time horizon. For this paper, all our models have been evaluated.  ... 
doi:10.2478/v10158-010-0018-09 fatcat:6cr57cthifbejmhhdau7cg34pm

Confident Classification using a Hybrid between Deterministic and Probabilistic Convolutional Neural Networks

Muhammad Naseer Bajwa, Suleman Khurram, Mohsin Munir, Shoaib Ahmed Siddiqui, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed
2020 IEEE Access  
INDEX TERMS Bayesian estimation, convolutional neural networks, hybrid neural networks, image classification, time-series classification, uncertainty estimation. 115476 This work is licensed under a Creative  ...  This paper proposes a hybrid convolutional neural network which combines high accuracy of deterministic models with posterior distribution approximation of Bayesian neural networks.  ...  TIME AND SPACE COMPLEXITY ANALYSIS The proposed hybrid model uses fewer parameters than its Bayesian counterpart as is evident from Table 1. The table shows III.  ... 
doi:10.1109/access.2020.3004409 fatcat:dvpcs3c57rg2tpp74xwjwky5wa

Target identification with dynamic hybrid Bayesian networks

Sampsa K. Hautaniemi, Petri T. Korpisaari, Jukka P. P. Saarinen, Sebastiano B. Serpico
2001 Image and Signal Processing for Remote Sensing VI  
In addition to the quite well-known theory of discrete and continuous Bayesian networks, we introduce a reasoning scheme to the hybrid Bayesian networks.  ...  Then the theories of discrete and continuous Bayesian networks are briefly introduced. The theory of the hybrid Bayesian network is represented in detail.  ...  DYNAMIC HYBRID BAYESIAN NETWORKS Bayesian networks are time invariant. That is, the a priori distributions at every moment t are the same.  ... 
doi:10.1117/12.413885 fatcat:4jbhakig3jat5cxqrjojhod35m

A Hybrid Recommender System Based on AHP That Awares Contexts with Bayesian Networks for Smart TV [chapter]

Ji-Chun Quan, Sung-Bae Cho
2014 Lecture Notes in Computer Science  
The accuracy of the Bayesian network model is improved through parameter learning from users' watching history.  ...  Experiments verify the inference accuracy of the Bayesian network and the accuracy of programs recommended by the proposed method.  ...  . • A hybrid method of AHP and Bayesian networks: The proposed method predicts group preference using a hybrid method of AHP and Bayesian networks which can solve the consistency problem of AHP.  ... 
doi:10.1007/978-3-319-07617-1_46 fatcat:djqsb26plvfwxi3qjw6yr7i6eq

Hybrid Intrusion Detection and Prediction multiAgent System HIDPAS [article]

Farah Jemili, Montaceur Zaghdoud, Mohamed Ben Ahmed
2009 arXiv   pre-print
This paper proposes an intrusion detection and prediction system based on uncertain and imprecise inference networks and its implementation.  ...  HYBRID PROPAGATION IN BAYESIAN NETWORKS The mechanism of propagation is based on Bayesian model. Therefore, the junction tree algorithm is used for the inference in the Bayesian network.  ...  This is due to the use of bayesian networks and the hybrid propagation within bayesian networks which is especially useful when dealing with missing information.  ... 
arXiv:0909.4889v1 fatcat:e3y6mtgewjfsvloww6u3vey3gu

Efficient inferencing for sigmoid Bayesian networks by reducing sampling space

Young S. Han, Young C. Park, Key-Sun Choi
1996 Applied intelligence (Boston)  
In this paper we show that the node reduction method that is an inferencing algorithm for general Bayesian networks can also be used on sigmoid Bayesian networks, and we propose a hybrid inferencing method  ...  The time efficiency of sampling after node reduction is demonstrated through experiments. The results of this paper bring sigmoid Bayesian networks closer to large scale applications.  ...  The time complexity of an arc reversal in sigmoid Bayesian networks is 0 ( N2) where N is the number of nodes of a given network.  ... 
doi:10.1007/bf00132734 fatcat:36wdaxkz25h6rczu6xvqf4lcam

Bayesian Modeling of Nonlinear Poisson Regression with Artificial Neural Networks [article]

Hansapani Rodrigo, Chris Tsokos
2018 arXiv   pre-print
In this study, we introduce a probabilistically driven nonlinear Poisson regression model with Bayesian artificial neural networks (ANN) to model count or rate data.  ...  This new nonlinear Poisson regression model developed with Bayesian ANN provides higher prediction accuracies over traditional Poisson or negative binomial regression models as revealed in our simulation  ...  ANN models with new hybrid Bayesian learning were better 100% of the time (in all 5 data sets) with regards to RMSE and RSE rates while they were better 60% of the time with regards to the MAE rates.  ... 
arXiv:1810.10138v1 fatcat:ronwejiw2jcyhpf62rucrzxhbm

Student's Academic Performance Prediction Using Factor Analysis Based Neural Network

Shamsuddeen Suleiman, Ahmad Lawal, Umar Usman, Shehu Usman Gulumbe, Aminu Bui Muhammad
2019 International Journal of Data Science and Analysis  
In a nutshell, the finding indicates that Bayesian Regularization is the best learning algorithms in both Neural Network and Hybrid models for predicting students' academic performance.  ...  The results showed ten new factors were successfully constructed using factor analysis and the proposed hybrid models show that though it took longer time and number of epochs to train the hybrid models  ...  Conclusion The results of the proposed hybrid models show that though it took longer time and number of epochs to train the hybrid model by Bayesian Regularization Algorithms, and it gives more accurate  ... 
doi:10.11648/j.ijdsa.20190504.12 fatcat:uwo7rpgfine2phtjobx2dm4lwu

Uncemented Arthroplasty for Hip Pain and Fracture after Metastatic Disease and Multiple Myeloma: Case Series, Exploratory Graphical Analysis and Bayesian Network Modeling

Sergio PS Meirelles, Daniel CS Rebolledo
2015 Journal of Orthopedic Oncology  
Objective: To describe a case series using a combination of narrative, graphical exploratory analysis and Bayesian Network modeling.  ...  Uncemented and hybrid arthroplasty devices did not differ in relation to time to walk as well as length of stay in this sample.  ...  Second, we develop a Bayesian Network model to compare uncemented vs hybrid hip arthroplasty devices in relation to time until first walk and length of stay.  ... 
doi:10.4172/joo.1000103 fatcat:lwywdodvxfbb5lzzowklqkcczm

Uncemented Arthroplasty for Hip Pain and Fracture after Metastatic Disease and Multiple Myeloma: Case Series, Exploratory Graphical Analysis and Bayesian Network Modeling

Sergio PS Meirelles, Daniel CS Rebolledo, Luiz FM Correia, Andre M Baptista, Olavo P Camargo
2015 Journal of Orthopedic Oncology  
Objective: To describe a case series using a combination of narrative, graphical exploratory analysis and Bayesian Network modeling.  ...  Uncemented and hybrid arthroplasty devices did not differ in relation to time to walk as well as length of stay in this sample.  ...  Second, we develop a Bayesian Network model to compare uncemented vs hybrid hip arthroplasty devices in relation to time until first walk and length of stay.  ... 
doi:10.4172/2472-016x.1000103 fatcat:li472txcg5fglglqt4abo4a5gy
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