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32
The maxmin hillclimbing bayesian network structure learning algorithm
 Machine Learning
, 2006
"... Abstract. We present a new algorithm for Bayesian network structure learning, called MaxMin HillClimbing (MMHC). The algorithm combines ideas from local learning, constraintbased, and searchandscore techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian n ..."
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Cited by 75 (7 self)
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Abstract. We present a new algorithm for Bayesian network structure learning, called MaxMin HillClimbing (MMHC). The algorithm combines ideas from local learning, constraintbased, and searchandscore techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesianscoring greedy hillclimbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and stateoftheart algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at
Stratified exponential families: Graphical models and model selection
 ANNALS OF STATISTICS
, 2001
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Algorithms for Large Scale Markov Blanket Discovery
 In The 16th International FLAIRS Conference, St
, 2003
"... This paper presents a number of new algorithras for discovering the Markov Blanket of a target variable T from training data. The Markov Blanket can be used for variable selection for classification, for causal discovery, and for Bayesian Network learning. We introduce a loworder polynomial algorit ..."
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Cited by 28 (4 self)
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This paper presents a number of new algorithras for discovering the Markov Blanket of a target variable T from training data. The Markov Blanket can be used for variable selection for classification, for causal discovery, and for Bayesian Network learning. We introduce a loworder polynomial algorithm and several variants that soundly induce the Markov Blanket under certain broad conditions in datasets with thousands of variables and compare them to other stateoftheart local and global methods with excellent results.
Time and Sample Efficient Discovery of Markov Blankets And Direct Causal Relations
 Proceedings of the 9th CAN SIGKDD International Conference on Knowledge Discovery and Data Mining
, 2003
"... Data Mining with Bayesian Network learning has two important characteristics: under broad conditions learned edges between variables correspond to causal influences, and second, for every variable T in the network a special subset (Markov Blanket) identifiable by the network is the mini mal variabl ..."
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Cited by 28 (7 self)
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Data Mining with Bayesian Network learning has two important characteristics: under broad conditions learned edges between variables correspond to causal influences, and second, for every variable T in the network a special subset (Markov Blanket) identifiable by the network is the mini mal variable set required to predict T. However, all known algorithms learning a complete BN do not scale up beyond a few hundred variables. On the other hand, all known sound algorithms learning a local region of the network require an exponential number of training instances to the size of the learned region.
A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We propose a new scoring function for learning Bayesian networks from data using score search algorithms. This is based on the concept of mutual information and exploits some wellknown properties of this measure in a novel way. Essentially, a statistical independence test based on the chisquare di ..."
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Cited by 17 (0 self)
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We propose a new scoring function for learning Bayesian networks from data using score search algorithms. This is based on the concept of mutual information and exploits some wellknown properties of this measure in a novel way. Essentially, a statistical independence test based on the chisquare distribution, associated with the mutual information measure, together with a property of additive decomposition of this measure, are combined in order to measure the degree of interaction between each variable and its parent variables in the network. The result is a nonBayesian scoring function called MIT (mutual information tests) which belongs to the family of scores based on information theory. The MIT score also represents a penalization of the KullbackLeibler divergence between the joint probability distributions associated with a candidate network and with the available data set. Detailed results of a complete experimental evaluation of the proposed scoring function and its comparison with the wellknown K2, BDeu and BIC/MDL scores are also presented.
Searching for Bayesian Network Structures in the Space of Restricted Acyclic Aprtially Directed Graphs
 Journal of Artificial Intelligence Research
, 2003
"... Although many algorithms have been designed to construct Bayesian network structures using dierent approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). ..."
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Cited by 15 (2 self)
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Although many algorithms have been designed to construct Bayesian network structures using dierent approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). Within the score+search paradigm, the dominant approach uses local search methods in the space of directed acyclic graphs (DAGs), where the usual choices for de ning the elementary modi cations (local changes) that can be applied are arc addition, arc deletion, and arc reversal. In this paper, we propose a new local search method that uses a dierent search space, and which takes account of the concept of equivalence between network structures: restricted acyclic partially directed graphs (RPDAGs). In this way, the number of dierent con gurations of the search space is reduced, thus improving eciency. Moreover, although the nal result must necessarily be a local optimum given the nature of the search method, the topology of the new search space, which avoids making early decisions about the directions of the arcs, may help to nd better local optima than those obtained by searching in the DAG space.
Q.: Learning Bayesian network equivalence classes with ant colony optimization
 Journal of Artificial Intelligence Research
, 2009
"... Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm called ACOE, to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimiz ..."
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Cited by 10 (2 self)
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Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm called ACOE, to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and implemented. Firstly, multiple types of moves are allowed. Secondly, moves can be given in terms of indices that are not based on construction graph nodes. The results of testing show that ACOE performs better than a greedy search and other stateoftheart and metaheuristic algorithms whilst searching in the space of equivalence classes. 1.
Bayesian networks for probabilistic weather prediction
 In Proceedings of the 15th Eureopean Conference on Artificial Intelligence, ECAI’2002
, 2002
"... Abstract. Several standard approaches have been introduced for meteorological time series prediction (analog techniques, neural networks, etc.). However, when dealing with multivariate spatially distributed time series (e.g., a network of meteorological stations over the Iberian peninsula) the above ..."
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Cited by 4 (0 self)
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Abstract. Several standard approaches have been introduced for meteorological time series prediction (analog techniques, neural networks, etc.). However, when dealing with multivariate spatially distributed time series (e.g., a network of meteorological stations over the Iberian peninsula) the above methods do not consider all the available information (they consider special independency assumptions to simplify the model). In this work, we introduce Bayesian Networks (BNs) in this framework to model the spatial and temporal dependencies among the different stations using a directed acyclic graph. This graph is learnt from the available databases and allows deriving a probabilistic model consistent with all the available information. Afterwards, the resulting model is combined with numerical atmospheric predictions which are given as evidence for the model. Effıcient inference mechanisms provide the conditional distributions of the desired variables at a desired future time. We illustrate the effıciency of the proposed methodology by obtaining precipitation forecasts for 100 stations in the North basin of the Iberian peninsula during Winter 1999. We show how standard analog techniques are a special case of the proposed methodology when no spatial dependencies are considered in the model. 1
Stakeholder values and scientific modelling in the Neuse river watershed,” Group Decision and Negotiation
, 2001
"... In 1998, the North Carolina Legislature mandated a 30 % reduction in the nitrogen loading in the Neuse River in an attempt to reduce undesirable environmental conditions in the lower river and estuary. Although sophisticated scientific models of the Neuse estuary exist, there is currently no study d ..."
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Cited by 4 (0 self)
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In 1998, the North Carolina Legislature mandated a 30 % reduction in the nitrogen loading in the Neuse River in an attempt to reduce undesirable environmental conditions in the lower river and estuary. Although sophisticated scientific models of the Neuse estuary exist, there is currently no study directly relating the nitrogenreduction policy to the concerns of the estuarine system’s stakeholders. Much of the difficulty lies in the fact that existing scientific models have biophysical outcome variables, such as dissolved oxygen, that are typically not directly meaningful to the public. In addition, stakeholders have concerns related to economics, modeling, implementation, and fairness that go beyond ecological outcomes. We describe a decisionanalytic approach to modeling the Neuse River nutrientmanagement problem, focusing on linking scientific assessments to stakeholder objectives. The first step in the approach is elicitation and analysis of stakeholder concerns. The second step is construction of a probabilistic model that relates proposed management actions to attributes of interest to stakeholders. We discuss how the model can then be used by local decision makers as a tool for adaptive management of the Neuse River system. This discussion relates adaptive management to the notion of expected value of information and indicates a need for a comprehensive monitoring program to accompany implementation of the model. We conclude by acknowledging that a scientific model cannot appropriately address all the stakeholder concerns elicited, and we discuss how the remaining concerns may otherwise be considered in the policy process.