Results 21  30
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148
Proactive Network Fault Detection
 IEEE Transactions on Reliability
, 1997
"... To improve network reliability and management in today's highspeed communication networks, we propose an intelligent system using adaptive statistical approaches. The system learns the normal behavior of the network. Deviations from the norm are detected and the information is combined in the proba ..."
Abstract

Cited by 63 (4 self)
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To improve network reliability and management in today's highspeed communication networks, we propose an intelligent system using adaptive statistical approaches. The system learns the normal behavior of the network. Deviations from the norm are detected and the information is combined in the probabilistic framework of a Bayesian network. The proposed system is thereby able to detect unknown or unseen faults. As demonstrated on real network data, this method can detect abnormal behavior before a fault actually occurs, giving the network management system (human or automated) the ability to avoid a potentially serious problem. 1 1
A Bayesian Approach to Tackling Hard Computational Problems
 IN UAI
, 2001
"... We describe research and results centering on the construction and use of Bayesian models that can predict the run time of problem solvers. Our efforts are motivated by observations of high variance in the time required to solve instances for several challenging problems. The methods ..."
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Cited by 63 (9 self)
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We describe research and results centering on the construction and use of Bayesian models that can predict the run time of problem solvers. Our efforts are motivated by observations of high variance in the time required to solve instances for several challenging problems. The methods
Decomposable Graphical Gaussian Model Determination
, 1999
"... We propose a methodology for Bayesian model determination in decomposable graphical gaussian models. To achieve this aim we consider a hyper inverse Wishart prior distribution on the concentration matrix for each given graph. To ensure compatibility across models, such prior distributions are obt ..."
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Cited by 63 (11 self)
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We propose a methodology for Bayesian model determination in decomposable graphical gaussian models. To achieve this aim we consider a hyper inverse Wishart prior distribution on the concentration matrix for each given graph. To ensure compatibility across models, such prior distributions are obtained by marginalisation from the prior conditional on the complete graph. We explore alternative structures for the hyperparameters of the latter, and their consequences for the model. Model determination is carried out by implementing a reversible jump MCMC sampler. In particular, the dimensionchanging move we propose involves adding or dropping an edge from the graph. We characterise the set of moves which preserve the decomposability of the graph, giving a fast algorithm for maintaining the junction tree representation of the graph at each sweep. As state variable, we propose to use the incomplete variancecovariance matrix, containing only the elements for which the correspondi...
A Bayesian approach to learning causal networks
 In Uncertainty in AI: Proceedings of the Eleventh Conference
, 1995
"... Whereas acausal Bayesian networks represent probabilistic independence, causal Bayesian networks represent causal relationships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal networks are fairly well developed. These methods oft ..."
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Cited by 57 (11 self)
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Whereas acausal Bayesian networks represent probabilistic independence, causal Bayesian networks represent causal relationships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal networks are fairly well developed. These methods often employ assumptions to facilitate the construction of priors, including the assumptions of parameter independence, parameter modularity, and likelihood equivalence. We show that although these assumptions also can be appropriate for learning causal networks, we need additional assumptions in order to learn causal networks. We introduce two sufficient assumptions, called mechanism independence and component independence. We show that these new assumptions, when combined with parameter independence, parameter modularity, and likelihood equivalence, allow us to apply methods for learning acausal networks to learn causal networks. 1
Robust Learning with Missing Data
, 1996
"... Bayesian methods are becoming increasingly popular in the development of intelligent machines. Bayesian Belief Networks (bbns) are nowaday a prominent reasoning method and, during the past few years, several efforts have been addressed to develop methods able to learn bbns directly from databases. H ..."
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Cited by 48 (5 self)
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Bayesian methods are becoming increasingly popular in the development of intelligent machines. Bayesian Belief Networks (bbns) are nowaday a prominent reasoning method and, during the past few years, several efforts have been addressed to develop methods able to learn bbns directly from databases. However, all these methods assume that the database is complete or, at least, that unreported data are missing at random. Unfortunately, realworld databases are rarely complete and the "Missing at Random" assumption is often unrealistic. This paper shows that this assumption can dramatically affect the reliability of the learned bbn and introduces a robust method to learn conditional probabilities in a bbn, which does not rely on this assumption. In order to drop this assumption, we have to change the overall learning strategy used by traditional Bayesian methods: our method bounds the set of all posterior probabilities consistent with the database and proceed by refining this set as more i...
Asymptotic model selection for directed networks with hidden variables
, 1996
"... We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be used to select models given large samples of data. The standard BIC as well as our extension punishes the complexity of a ..."
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Cited by 46 (13 self)
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We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be used to select models given large samples of data. The standard BIC as well as our extension punishes the complexity of a model according to the dimension of its parameters. We argue that the dimension of a Bayesian network with hidden variables is the rank of the Jacobian matrix of the transformation between the parameters of the network and the parameters of the observable variables. We compute the dimensions of several networks including the naive Bayes model with a hidden root node. 1
Learning Bayesian Networks: A unification for discrete and Gaussian domains
 PROCEEDINGS OF ELEVENTH CONFERENCE ON UNCERTAINTY INARTI CIAL INTELLIGENCE
, 1995
"... We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data. In particular, we unify the approaches we presented at last year's conference for discrete and Gaussian domains. We derive a general Bayesian scoring metric, appropriate for both do ..."
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Cited by 43 (5 self)
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We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data. In particular, we unify the approaches we presented at last year's conference for discrete and Gaussian domains. We derive a general Bayesian scoring metric, appropriate for both domains. We then use this metric in combination with wellknown statistical facts about the Dirichlet and normal{Wishart distributions to derive our metrics for discrete and Gaussian domains.
Accounting for Model Uncertainty in Survival Analysis Improves Predictive Performance
 In Bayesian Statistics 5
, 1995
"... Survival analysis is concerned with finding models to predict the survival of patients or to assess the efficacy of a clinical treatment. A key part of the modelbuilding process is the selection of the predictor variables. It is standard to use a stepwise procedure guided by a series of significanc ..."
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Cited by 39 (12 self)
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Survival analysis is concerned with finding models to predict the survival of patients or to assess the efficacy of a clinical treatment. A key part of the modelbuilding process is the selection of the predictor variables. It is standard to use a stepwise procedure guided by a series of significance tests to select a single model, and then to make inference conditionally on the selected model. However, this ignores model uncertainty, which can be substantial. We review the standard Bayesian model averaging solution to this problem and extend it to survival analysis, introducing partial Bayes factors to do so for the Cox proportional hazards model. In two examples, taking account of model uncertainty enhances predictive performance, to an extent that could be clinically useful. 1 Introduction From 1974 to 1984 the Mayo Clinic conducted a doubleblinded randomized clinical trial involving 312 patients to compare the drug DPCA with a placebo in the treatment of primary biliary cirrhosis...
Dynamic Workflow Composition using Markov Decision Processes
 International Journal of Web Services Research
, 2005
"... The advent of Web services has made automated workflow composition relevant to web based applications. One technique, that has received some attention, for automatically composing workflows is AIbased classical planning. However, classical planning suffers from the paradox of first assuming determi ..."
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Cited by 37 (10 self)
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The advent of Web services has made automated workflow composition relevant to web based applications. One technique, that has received some attention, for automatically composing workflows is AIbased classical planning. However, classical planning suffers from the paradox of first assuming deterministic behavior of Web services, then requiring the additional overhead of execution monitoring to recover from unexpected behavior of services. To address these concerns, we propose using Markov decision processes (MDPs), to model workflow composition. Our method models both, the inherent stochastic nature of Web services, and the dynamic nature of the environment. The resulting workflows are robust to nondeterministic behaviors of Web services and adaptive to a changing environment. Using an example scenario, we demonstrate our method and provide empirical results in its support. 1.