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Exact structure learning of Bayesian networks by optimal path extension

Subhadeep Karan, Jaroslaw Zola
2016 2016 IEEE International Conference on Big Data (Big Data)  
The problem of exact structure learning is to find a network structure that is optimal under certain scoring criteria.  ...  In this paper, we introduce a new approach for exact structure learning.  ...  CONCLUSION In this paper, we presented a new approach to accelerate the exact structure learning of Bayesian networks.  ... 
doi:10.1109/bigdata.2016.7840588 dblp:conf/bigdataconf/KaranZ16 fatcat:c32jx3fk3rd5zgugc6qex74erq

Learning Bayesian Networks with Non-Decomposable Scores [chapter]

Eunice Yuh-Jie Chen, Arthur Choi, Adnan Darwiche
2015 Lecture Notes in Computer Science  
Modern approaches for optimally learning Bayesian network structures require decomposable scores. Such approaches include those based on dynamic programming and heuristic search methods.  ...  In this paper, we break from this tradition, and show that one can effectively learn structures using non-decomposable scores by exploring a more complex search space that leverages state-of-the-art learning  ...  We also thank James Cussens and Brandon Malone for their comments on an earlier version of this paper. This work was supported in part by ONR grant #N00014-12-1-0423 and NSF grant #IIS-1514253.  ... 
doi:10.1007/978-3-319-28702-7_4 fatcat:v6zmmyk6orhrpkpi4wuofsslbm

Learning bayesian networks consistent with the optimal branching

Alexandra M. Carvalho, Arlindo L. Oliveira
2007 Sixth International Conference on Machine Learning and Applications (ICMLA 2007)  
We introduce a polynomial-time algorithm to learn Bayesian networks whose structure is restricted to nodes with in-degree at most k and to edges consistent with the optimal branching, that we call consistent  ...  The optimal branching is used as an heuristic for a primary causality order between network variables, which is subsequently refined, according to a certain score, into an optimal CkG Bayesian network.  ...  This work was partially supported by EU FEDER via FCT project POSC/EIA/ 57398/2004.  ... 
doi:10.1109/icmla.2007.74 dblp:conf/icmla/CarvalhoO07 fatcat:rdp53hwprbf4daj7nezufsezmy

Finding the optimal Bayesian network given a constraint graph

Jacob M. Schreiber, William S. Noble
2017 PeerJ Computer Science  
Despite recent algorithmic improvements, learning the optimal structure of a Bayesian network from data is typically infeasible past a few dozen variables.  ...  While super-structures and constraint rules are flexible in terms of what prior knowledge they can encode, they achieve savings in memory and computational time simply by avoiding considering invalid graphs  ...  Grant Disclosures The following grant information was disclosed by the authors: NSF IGERT: DGE-1258485.  ... 
doi:10.7717/peerj-cs.122 fatcat:jmaasajtcvcqxmx73nvwpcaski

Exact Bayesian network learning in estimation of distribution algorithms

Carlos Echegoyen, Jose A. Lozano, Roberto Santana, Pedro Larranaga
2007 2007 IEEE Congress on Evolutionary Computation  
The estimation of Bayesian network algorithm (EBNA) is used to analyze the impact of learning the optimal (exact) structure in the search.  ...  By applying recently introduced methods that allow learning optimal Bayesian networks, we investigate two important issues in EDAs.  ...  It has been also supported by the Spanish Ministerio de Ciencia y Tecnología under grant TIN 2005-03824.  ... 
doi:10.1109/cec.2007.4424586 dblp:conf/cec/EchegoyenLSL07 fatcat:2xh3mem275fsjdl2y556wodjmi

Bayesian networks

Adnan Darwiche
2010 Communications of the ACM  
Bayesian networks provide a systematic and localized method for structuring probabilistic information about a situation into a coherent whole, and are supported by a suite of inference algorithms.  ...  Bayesian network algorithms instead of having to invent specialized algorithms for each new application.  ...  been treated, 16,30 although not as extensively as the learning of general Bayesian networks.  ... 
doi:10.1145/1859204.1859227 fatcat:tp6zu5kxy5g7vp2wz7oupghxpi

Learning Optimal Bayesian Networks: A Shortest Path Perspective

C. Yuan, B. Malone
2013 The Journal of Artificial Intelligence Research  
One is an A* search algorithm that learns an optimal Bayesian network structure by only searching the most promising part of the solution space. The others are mainly two heuristic functions.  ...  In this paper, learning a Bayesian network structure that optimizes a scoring function for a given dataset is viewed as a shortest path problem in an implicit state-space search graph.  ...  Acknowledgments This research was supported by NSF grants IIS-0953723, EPS-0903787, IIS-1219114 and the Academy of Finland (Finnish Centre of Excellence in Computational Inference Research COIN, 251170  ... 
doi:10.1613/jair.4039 fatcat:xfdxrovzsjfwho3yqutf6e3b4m

Learning the dependency structure of highway networks for traffic forecast

Samitha Samaranayake, Sebastien Blandin, Alexandre Bayen
2011 IEEE Conference on Decision and Control and European Control Conference  
In this article, a Bayesian network framework is introduced to model the correlation structure of highway networks in the context of traffic forecast.  ...  We formulate the dependency learning problem as an optimization problem and propose an efficient algorithm to identify the inclusion-optimal dependency structure of the network given historical observations  ...  We learn the optimal structure of the Bayesian network on data corresponding to 2 hours of the morning rush on February 1st, 2010.  ... 
doi:10.1109/cdc.2011.6161510 dblp:conf/cdc/SamaranayakeBB11 fatcat:r2375wrpqzfnrpfvcpkasz7mya

Local Structure Discovery in Bayesian Networks [article]

Teppo Niinimaki, Pekka Parviainen
2012 arXiv   pre-print
Learning a Bayesian network structure from data is an NP-hard problem and thus exact algorithms are feasible only for small data sets.  ...  We also study the prospects of constructing the network structure for the whole node set based on local results by presenting two algorithms and comparing them to several heuristics.  ...  PRELIMINARIES BAYESIAN NETWORKS The structure of a Bayesian network is represented by a directed acyclic graph (DAG).  ... 
arXiv:1210.4888v1 fatcat:bpm267a5vrgd5gwc23xiigllgu

Development of a Multilayer Perception Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments

Joseph Isabona, Agbotiname Lucky Imoize, Stephen Ojo, Olukayode Karunwi, Yongsung Kim, Cheng-Chi Lee, Chun-Ta Li
2022 Applied Sciences  
In detail, the developed MLP model prediction accuracy level using different learning and training algorithms with the tuned best values of the hyperparameters have been applied for extensive path loss  ...  To constantly and reliably deploy and optimally manage such mobile cellular networks, the radio signal attenuation loss between the path lengths of a base transmitter and the mobile station receiver must  ...  Acknowledgments: The work of Agbotiname Lucky Imoize is supported in part by the Nigerian Petroleum Technology Development Fund (PTDF) and in part by the German Academic Exchange Service (DAAD) through  ... 
doi:10.3390/app12115713 fatcat:b434kacgkbcn3pluuzw7lbcv64

WiseR: An end-to-end structure learning and deployment framework for causal graphical models [article]

Shubham Maheshwari, Khushbu Pahwa, Tavpritesh Sethi
2021 arXiv   pre-print
We present wiseR, an open source application for learning, evaluating and deploying robust causal graphical models using graph neural networks and Bayesian networks.  ...  Structure learning offers an expressive, versatile and explainable approach to causal and mechanistic modeling of complex biological data.  ...  Acknowledgements This work was supported by the DBT/Wellcome Trust India Alliance Fellowship IA/CPHE/14/1/501504 awarded to Tavpritesh Sethi.  ... 
arXiv:2108.07046v2 fatcat:e5mnptvy65cd3j7kaa3alamela

Learning Large-Scale Bayesian Networks with the sparsebn Package

Bryon Aragam, Jiaying Gu, Qing Zhou
2019 Journal of Statistical Software  
To meet this challenge, we have developed a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks.  ...  To bridge this gap we have developed sparsebn, a new R (R Core Team 2019) package for structure learning and parameter estimation of large-scale Bayesian networks from highdimensional data.  ...  Acknowledgements This work was supported by NSF grants IIS-1546098 and DMS-1055286 to Q.Z. The authors thank Dacheng Zhang for helpful discussions and computational assistance.  ... 
doi:10.18637/jss.v091.i11 fatcat:ykeql47uabfxxgpjgupioxklxu

Characterization of Dynamic Bayesian Network-The Dynamic Bayesian Network as temporal network

Nabil Ghanmi, Mohamed Ali, Najoua Essoukri
2011 International Journal of Advanced Computer Science and Applications  
Then we will present different levels and methods of creating DBNs as well as approaches of incorporating temporal dimension in static Bayesian network.  ...  We start with basics of DBN where we especially focus in Inference and Learning concepts and algorithms.  ...  DIFFERENT APPROACHES FOR INCORPORATING TIME IN BAYESIAN NETWORK Dynamic Bayesian Networks (DBN) are an extension of Bayesian networks that represent the temporal or spatial evolution of random variables  ... 
doi:10.14569/ijacsa.2011.020708 fatcat:hf3ghy3wqjavthywervfj5az3i

Graphical Models in a Nutshell [chapter]

2007 Introduction to Statistical Relational Learning  
in large networks.  ...  Graphical models have enjoyed a surge of interest in the last two decades, due both to the flexibility and power of the representation and to the increased ability to effectively learn and perform inference  ...  Learning the Bayesian Network Structure Next we consider the problem of learning the structure of a Bayesian network.  ... 
doi:10.7551/mitpress/7432.003.0004 fatcat:wbhjah7qczdftaiod5jg4ok2xe

Inference and Learning in Multi-dimensional Bayesian Network Classifiers [chapter]

Peter R. de Waal, Linda C. van der Gaag
2007 Lecture Notes in Computer Science  
We describe the family of multi-dimensional Bayesian network classifiers which include one or more class variables and multiple feature variables.  ...  For the family of multidimensional classifiers, we address the complexity of the classification problem and show that it can be solved in polynomial time for classifiers with a graphical structure of bounded  ...  As for onedimensional Bayesian network classifiers, we distinguished between different types of multi-dimensional classifier by imposing restrictions on their graphical structure.  ... 
doi:10.1007/978-3-540-75256-1_45 fatcat:f6jp5ozco5h6dk3pvrmyui4jmy
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