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A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

Sho Fukuda, Yuuma Yamanaka, Takuya Yoshihiro
2014 International Journal of Interactive Multimedia and Artificial Intelligence  
To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve.  ...  In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks.  ...  To learn a near-optimal Bayesian network structure from a set of target data, efficient optimization algorithm is required that searches an exponentially large solution space for nearoptimal Bayesian network  ... 
doi:10.9781/ijimai.2014.311 fatcat:a5v633vmangvzkgzklavgydmn4

Research on the Product Configuration Method Based on Constraint Satisfaction Problem and Bayesian Network

Zhiqiang Liu, Yila Su, Huimin Li, Fei Wang
2015 International Journal of u- and e- Service, Science and Technology  
The structure is a tree, a special Bayesian network structure, the logical structure nodes of the product is used as Bayesian network nodes, which are also set as the user's preference, directed edges  ...  Determine the structure and parameters of Bayesian network that determines Bayesian network model, then you can use this model to do probabilistic reasoning.  ...  Acknowledgements This work is partially supported by National Natural Science Foundation of China  ... 
doi:10.14257/ijunesst.2015.8.4.26 fatcat:l2tvy6jecvfzxa5uhcfydsg3ru

Consistent Learning Bayesian Networks with Thousands of Variables

Kazuki Natori, Masaki Uto, Maomi Ueno
2017 Workshop on Advanced Methodologies for Bayesian Networks  
We have already proposed a constraint-based learning Bayesian network method using Bayes factor.  ...  This report describes some experiments related to the learning of large network structures. Results show that the proposed method can learn surprisingly huge networks with thousands of variables.  ...  Because the Bayesian network structure is generally unknown, it is necessary to estimate the structure of Bayesian network from observed data in a process called "learning Bayesian networks".  ... 
dblp:conf/ambn/NatoriUU17 fatcat:qmc2ivjcf5amxgik4kordqshgy

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  ...  : G(V, E) satisfies C Solving this problem gives us the structure of the Bayesian network most likely to explain the observed data given the modeling constraints.  ... 
doi:10.1109/cdc.2011.6161510 dblp:conf/cdc/SamaranayakeBB11 fatcat:r2375wrpqzfnrpfvcpkasz7mya

Learning Bounded Tree-Width Bayesian Networks via Sampling [chapter]

Siqi Nie, Cassio P. de Campos, Qiang Ji
2015 Lecture Notes in Computer Science  
Some existing methods [12, 14] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k.  ...  The proposed algorithm can efficiently learn a Bayesian network with tree-width at most k.  ...  Acknowledgements This work is supported in part by the grant N00014-12-1-0868 from the US Office of Navy Research.  ... 
doi:10.1007/978-3-319-20807-7_35 fatcat:fcxjip6vtjgqrdf445mh2hppdu

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.  ...  We introduce the concept of a "constraint graph" as an intuitive method for incorporating rich prior knowledge into the structure learning task.  ...  ACKNOWLEDGEMENTS We would like to acknowledge Maxwell Libbrecht, Scott Lundberg, and Brandon Malone for many useful discussions and comments on drafts of the paper.  ... 
doi:10.7717/peerj-cs.122 fatcat:jmaasajtcvcqxmx73nvwpcaski

Learning Bayesian networks with ancestral constraints

Eunice Yuh-Jie Chen, Yujia Shen, Arthur Choi, Adnan Darwiche
2016 Neural Information Processing Systems  
We consider the problem of learning Bayesian networks optimally, when subject to background knowledge in the form of ancestral constraints.  ...  The proposed framework exploits oracles for learning structures using decomposable scores, which cannot accommodate ancestral constraints since they are non-decomposable.  ...  constraints.  ... 
dblp:conf/nips/ChenSCD16 fatcat:heded6nw55g6pnrfqn3prvos3u

BNFinder: exact and efficient method for learning Bayesian networks

B. Wilczynski, N. Dojer
2008 Bioinformatics  
Motivation: Bayesian methods are widely used in many different areas of research.  ...  The main advantage of BNFinder is the use exact algorithm, which is at the same time very efficient (polynomial with respect to the number of observations).  ...  Since perturbations change the structure of interactions, learning procedures have to use data selectively.  ... 
doi:10.1093/bioinformatics/btn505 pmid:18826957 pmcid:PMC2639006 fatcat:zxa4gmzgznbvff44dmlwnqvjyq

Bayesian Networks: A State-Of-The-Art Survey

Nelda Kote, Marenglen Biba, Elena Canaj
2018 International Conference on Recent Trends and Applications in Computer Science and Information Technology  
The articles are classified based on a scheme that consists of three main Bayesian Networks topics: Bayesian Networks Structure Learning, Advanced Application of Bayesian Networks and Bayesian Network  ...  Over the last decade, Bayesian Networks (BNs) have become an increasingly popular Artificial Intelligence approach. BNs are a widely used method in the modelling of uncertain knowledge.  ...  Authors in [Li17] by combining the advantages of constraint-based and score-based algorithms, proposed a hybrid distributed Bayesian Network structure learning algorithm from large-scale dataset using  ... 
dblp:conf/rtacsit/KoteBC18 fatcat:75dzoowrbzbqdoqi7zqbt3xjfm

A theory of inferred causation [chapter]

Judea Pearl, Thomas S. Verma
1995 Studies in Logic and the Foundations of Mathematics  
arcs: cannot compensate using CPT learning; ignorance about causality • Solution Approaches -Constraint-based: enforce consistency of network with observations -Score-based: optimize degree of match between  ...  learning) -Some temporal models: Dynamic Bayesian Networks (DBNs) Continuing Research on Continuing Research on Learning Bayesian Networks from Data Learning Bayesian Networks from Data Kansas State University  ... 
doi:10.1016/s0049-237x(06)80074-1 fatcat:gihsurzxdjebhkbtilxcjlhtqi

A Bayesian Network Learning Algorithm Based on Independence Test and Ant Colony Optimization

Jun-Zhong JI, Hong-Xun ZHANG, Ren-Bing HU, Chun-Nian LIU
2009 Acta Automatica Sinica  
To solve the drawbacks of the ant colony optimization for learning Bayesian networks (ACO-B), this paper proposes an improved algorithm based on the conditional independence test and ant colony optimization  ...  First, the I-ACO-B uses order-0 independence tests to effectively restrict the space of candidate solutions, so that many unnecessary searches of ants can be avoided.  ...  Especially, there are three efficient approaches using the stochastic search mechanism to tackle the problem of learning Bayesian network.  ... 
doi:10.1016/s1874-1029(08)60077-4 fatcat:xfdidmbr4rg7lloh6wcjrlnh3q

Research on Dynamic Programming Strategy of Bayesian Network Structure Learning

Ruohai Di, Ye Li, Tingpeng Li, Peng Wang, Chuchao He
2022 Scientific Programming  
Bayesian network structure learning based on dynamic programming strategy can be used to find the optimal graph structure compared with approximate search methods.  ...  The traditional dynamic programming method for Bayesian network structure learning is a depth-first-based strategy, which is inefficient. We proposed two methods to solve this problem.  ...  Theoretical Basis of Bayesian Network In this section, we briefly introduce the basics of BN and the concepts that are used to learn the structure of these networks. Bayesian Network Definition 1.  ... 
doi:10.1155/2022/4391071 doaj:4e10c2b1a8664b1da26d75485a4d6c80 fatcat:pcarycz5d5ax7k5gwdvji3mcl4

Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering [article]

Yuhong Guo, Dale Schuurmans
2012 arXiv   pre-print
We present a new approach to learning the structure and parameters of a Bayesian network based on regularized estimation in an exponential family representation.  ...  Here we show that, given a fixed variable order, the optimal structure and parameters can be learned efficiently, even without restricting the size of the parent sets.  ...  Here, we propose an efficient relaxation of the Bayesian network structure learning problem-solving for the structural features that determine the graph, the variable ordering that determines the edge  ... 
arXiv:1206.6832v1 fatcat:ro5qobyavvgivcuknxgrqvtl24

aGrUM/pyAgrum : a toolbox to build models and algorithms for Probabilistic Graphical Models in Python

Gaspard Ducamp, Christophe Gonzales, Pierre-Henri Wuillemin
2020 European Workshop on Probabilistic Graphical Models  
This paper presents the aGrUM framework, a LGPL C++ library providing state-of-the-art implementations of graphical models for decision making, including Bayesian Networks, Markov Networks (Markov random  ...  This framework is the result of an ongoing effort to build an efficient and well maintained open source cross-platform software, running on Linux, MacOS X and Windows, for dealing with graphical models  ...  Other projects with Airbus Research and the OpenTURNS consortium use aGrUM for structural learning in non-parametric Copula Bayesian Networks.  ... 
dblp:conf/pgm/DucampGW20 fatcat:wiwzbpmogbdmzlcoxfknrypdjq

Advanced Algorithms of Bayesian Network Learning and Probabilistic Inference from Inconsistent Prior Knowledge and Sparse Data with Applications in Computational Biology and Computer Vision [chapter]

Rui Chang
2010 Bayesian Network  
Fruitful results have been achieved, especially in the efficient learning of Bayesian network structure and parameters with (in-) complete data (4; 19-21).  ...  ., the selection of a single best Bayesian network model from the data by learning, is useful for the case of large data sets, independence assumptions among the network variables often make this single  ...  This method is particular useful in accurate learning of a Bayesian network under sparse training data.  ... 
doi:10.5772/46967 fatcat:ijic5ya535bzdhk6vgv4hunyia
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