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Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets
[article]
2018
arXiv
pre-print
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Learning a Bayesian networks with bounded treewidth is important for reducing the complexity of the inferences. ...
Through extensive experiments we show that it consistently yields higher-scoring structures than its competitors on complete data sets. ...
Structure learning of Bayesian networks We consider the problem of learning the structure of a Bayesian network from a complete data set. ...
arXiv:1802.02468v1
fatcat:aixppnzv25a6vgvwcmjtzqdpd4
Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm
2008
Decision Support Systems
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This paper proposes a novel method for learning Bayesian networks from incomplete databases in the presence of missing values, which combines an evolutionary algorithm with the traditional Expectation ...
A data completing procedure is presented for learning and evaluating the candidate networks. Moreover, a strategy is introduced to obtain better initial networks to facilitate the method. ...
Thus the incomplete database may be a representative sample of the complete database. ...
doi:10.1016/j.dss.2008.01.002
fatcat:jk5wsgyonndytjeoiqofxbssw4
Are Security Experts Useful? Bayesian Nash Equilibria for Network Security Games with Limited Information
[chapter]
2010
Lecture Notes in Computer Science
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Level
6 Cooperative players
Complete info, or 0 Experts
1 Expert, incomplete info
2 Experts, incomplete info
3 Experts, incomplete info
4 Experts, incomplete info
5 Experts, incomplete info ...
Naive players are myopic; they behave as if H(e 1 , . . . , e n ) = e i • Complete vs incomplete information • An expert with complete information knows the expected losses for all players. ...
doi:10.1007/978-3-642-15497-3_36
fatcat:737e6i2x7ffefih6go4gmawhtq
Bayesian networks for supporting query processing over incomplete autonomous databases
2013
Journal of Intelligent Information Systems
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We learn this distribution in terms of Bayesian networks. ...
We present empirical studies to demonstrate that (i) at higher levels of incompleteness, when multiple attribute values are missing, Bayesian networks do provide a significantly higher classification accuracy ...
The structure of the Bayesian network is learned from a complete sample of the autonomous database. ...
doi:10.1007/s10844-013-0277-0
fatcat:ghxs4a4t6zgzfpcgt2kglizbou
Price of Anarchy of Network Routing Games with Incomplete Information
[chapter]
2005
Lecture Notes in Computer Science
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In this paper, we first set up a model for such network situations; the model is a noncooperative Bayesian game with incomplete information. ...
This fact, in particular, implies that the same bounds must hold for the complete information case, which is vindicated by the existing results in the literature for complete information routing games. ...
In this more realistic scenario, the underlying network routing game becomes a Bayesian game of incomplete information. ...
doi:10.1007/11600930_107
fatcat:uf4du42kljaljk5g4zpvwcgkbi
Maximum entropy and least square error minimizing procedures for estimating missing conditional probabilities in Bayesian networks
2008
Computational Statistics & Data Analysis
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Conditional probability tables (CPT) in many Bayesian networks often contain missing values. ...
The ME and least square (LS) error minimizing approaches can be used for estimating missing values in CPT for Bayesian networks. ...
While the data set might appear complete, when a Bayesian network is constructed using the data, the missing combinations will lead to missing values in conditional probability tables for the Bayesian ...
doi:10.1016/j.csda.2007.11.013
fatcat:v6fnerwzg5fqppx7lrp7oqhnnu
Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling
2003
Science
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• Both exploit inference procedures for Bayesian networks to compute expected sufficient statistics networks to compute expected sufficient statistics Incomplete Data: Structure Scores Incomplete Data ...
Network Bayesian Network • Directed acyclic graph (DAG). ...
Learning Bayesian Network Learning Bayesian Network • Score: Bayesian score metric (BSM). • Local heuristic searching algorithm: simulated annealing. g • Caching: keeping the top 500 structures recorded ...
doi:10.1126/science.1081900
pmid:12843395
fatcat:gd4eqdnsbjdhdjpsf5aik44kxi
Analyzing Linked Data Quality with LiQuate
[chapter]
2014
Lecture Notes in Computer Science
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We demonstrate the capabilities of LiQuate (Linked Data Quality Assessment), a tool that relies on Bayesian Networks to analyze the quality of data and links in the LOD cloud. ...
However, because of data source heterogeneity, published data may suffer of redundancy, inconsistencies, or may be incomplete; thus, results generated by LOD-based applications may be imprecise, ambiguous ...
The Bayesian Network Query Translator considers the user request and generates the set of queries that must be posed against the Bayesian Network. ...
doi:10.1007/978-3-319-11955-7_72
fatcat:u43xtwwmxrdd3alcdr6nz3swfe
The threshold EM algorithm for parameter learning in bayesian network with incomplete data
2011
International Journal of Advanced Computer Science and Applications
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Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. ...
This range is calculated by the first step of RBE algorithm allowing a regularization of each parameter in bayesian network after the maximization step of the EM algorithm. ...
THE THRESHOLD EM ALGORITHM FOR PARAMETER LEARNING IN BAYESIAN NETWORK WITH INCOMPLETE DATA The set of parameter in bayesian network using EM algorithm is approximate. ...
doi:10.14569/ijacsa.2011.020713
fatcat:jwta4k2pbjdndnwgxtfmodg3zi
The threshold EM algorithm for parameter learning in bayesian network with incomplete data
[article]
2012
arXiv
pre-print
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Bayesian networks (BN) are used in a big range of applications but they have one issue concerning parameter learning. ...
This range is calculated by the first step of RBE algorithm allowing a regularization of each parameter in bayesian network after the maximization step of the EM algorithm. ...
THE THRESHOLD EM ALGORITHM FOR PARAMETER LEARNING IN BAYESIAN NETWORK WITH INCOMPLETE DATA The set of parameter in bayesian network using EM algorithm is approximate. ...
arXiv:1204.1681v1
fatcat:7kqyiwf7tfel7jc4zbue42fmhy
A Bayesian Network Approach to Traffic Flow Forecasting
2006
IEEE transactions on intelligent transportation systems (Print)
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flow modeling and forecasting, both for complete data and incomplete data. ...
In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. ...
MODEL CONSTRUCTION AND EXPERIMENTS FOR INCOMPLETE DATA In Section III, we applied the Bayesian network approach to carry out traffic flow modeling and forecasting in case of complete data. ...
doi:10.1109/tits.2006.869623
fatcat:w3ij57fabnhkbgh2wquu2h2h3e
Price of Stability in Games of Incomplete Information
[article]
2015
arXiv
pre-print
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whose complete information counterparts have been well-studied, such as undirected network design games, multi-cast games and covering games. ...
We apply our approach to Bayesian versions of the archetypal, in the price of stability analysis, network design models and show the existence of O((n))-approximate Bayes-Nash equilibria in several games ...
One of the engineering oriented motivations of analyzing the Bayesian price of stability in such network design games of incomplete information is similar to their complete information counterparts: the ...
arXiv:1503.03739v1
fatcat:wv53koe7svewtlqfaroq323dtm
Maximal prime subgraph decomposition of Bayesian networks
2002
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
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The correctness of the method is proven and results relating the maximal prime subgraph decomposition to the maximal complete subgraphs of the moral graph of the original Bayesian network are presented ...
In this paper we present a method for decomposition of Bayesian networks into their maximal prime subgraphs. ...
Cliques BLS ~md BEL are aggregated to form the MPD junction tree since the separator BL is incomplete in G M. []
E Figure 5 : A junction tree for the Bayesian network shown in figure 1 . ...
doi:10.1109/3477.979956
pmid:18238100
fatcat:vg4qovtxszbshcnu2lwrq33ikq
The AI&M Procedure for Learning from Incomplete Data
[article]
2012
arXiv
pre-print
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We apply the AI&M method to learning parameters for Bayesian networks. The method is compared against conservative inference, which takes into account each possible data completion, and against EM. ...
We investigate methods for parameter learning from incomplete data that is not missing at random. ...
We close this section by remarking that for Bayesian networks an alternative to AI&M exists in learning a Bayesian network B that extends the given complete data network B with observation nodes (cf. ...
arXiv:1206.6830v1
fatcat:ubrog5cphnfubefgfnpjrop7nq
The Impact of Incomplete Information on Games in Parallel Relay Networks
[article]
2011
arXiv
pre-print
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We consider the impact of incomplete information on incentives for node cooperation in parallel relay networks with one source node, one destination node, and multiple relay nodes. ...
We examine different bargaining relationships between the source and the relays, and propose a framework for analyzing the efficiency loss induced by incomplete information. ...
All the above papers assume a complete information setting where players in the network game have complete knowledge about quantities such as the state of network links. ...
arXiv:1101.4063v1
fatcat:vs22e7knlvdplj357ndof24yxa
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