Learning Bayesian networks: approaches and issues

Daly, Rónán; Shen, Qiang; Aitken, Stuart

doi:10.1017/s0269888910000251

Cited by 271 publications

(203 citation statements)

References 317 publications

(645 reference statements)

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“…The direction of inference is usually predefined because learning the direction of inference is too slow for many online learning tasks due to that conditional probabilities are usually unidirectional while the direction of inference is not obvious from the data. The inference structure learning can be seen as choosing a model from all possible inference networks that represents the data, which has been shown to be NPHard [15]. Bayesian networks with predefined inference structure have been used to interpret percept sequences, to derive possible adversarial goals and actions by computing the posterior probabilities of goals, states, and plans given the percept sequences [16].…”

Section: Bayesian Networkmentioning

confidence: 99%

Learning and prediction of relational time series

Tan

Darken

2015

Comput Math Organ Theory

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to 12a. DISTRIBUTION / AVAILABILITY STATEMENTApproved for public release; distribution is unlimited 12b. DISTRIBUTION CODE ABSTRACT (maximum 200 words)Prediction of events is fundamental to both human and artificial agents. The main problem with previous prediction techniques is that they cannot predict events that have never been experienced before. This dissertation addresses the problem of predicting such novelty by developing algorithms and computational models inspired from recent cognitive science theories: conceptual blending theory and event segmentation theory. We were able to show that prediction accuracy for event or state prediction can be significantly improved using these methods.The main contribution of this dissertation is a new class of prediction techniques inspired by conceptual blending that improves prediction accuracy overall and has the ability to predict even events that have never been experienced before. We also show that event segmentation theory, when integrated with these techniques, results in greater computational efficiency. We implemented the new prediction techniques, and more traditional alternatives such as Markov and Bayesian techniques, and compared their prediction accuracy quantitatively for three domains: a roleplaying game, intrusion-system alerts, and event prediction of maritime paths in a discrete-event simulator. Other contributions include two new unification algorithms that improve over a naïve one, and an exploration of ways to maintain a minimum-size knowledge base without affecting prediction accuracy. SUBJECT TERMS ABSTRACTPrediction of events is fundamental to both human and artificial agents. The main problem with previous prediction techniques is that they cannot predict events that have never been experienced before. This dissertation addresses the problem of predicting such novelty by developing algorithms and computational models inspired from recent cognitive science theories: conceptual blending theory and event segmentation theory.We were able to show that prediction accuracy for event or state prediction can be significantly improved using these methods.The main contribution of this dissertation is a new class of prediction techniques inspired by conceptual blending that improves prediction accuracy overall and has the ability to predict even events that have never been experienced before. We also show that event segmentation theory, when integrated with these techniques, results in greater computational efficiency. We implemented the new prediction techniques, and more traditional alternatives such as Markov and Bayesian t...

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Section: Bayesian Networkmentioning

confidence: 99%

Learning and prediction of relational time series

Tan

Darken

2015

Comput Math Organ Theory

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“…Dynamic Bayesian networks that are mainly used to learn and reproduce time--dependent system be--havior (Daly et al 2011) process uncertain knowledge in a time--dynamic model. However, this ap--proach focuses on variances in state transitions and does not include flow--specific behavior.…”

Section: Introductionmentioning

confidence: 99%

A dynamic probabilistic material flow modeling method

Bornhöft

Sun

Hilty

et al. 2016

Environmental Modelling & Software

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Material flow modeling constitutes an important approach to predicting and understanding the flows of materials through the anthroposphere into the environment. The new "Dynamic Probabilistic Material Flow Analysis (DPMFA)" method, combining dynamic material flow modeling with probabilistic modeling, is presented in this paper. Material transfers that lead to particular environmental stocks are represented as systems of mass-balanced flows. The time-dynamic behavior of the system is calculated by adding up the flows over several consecutive periods, considering changes in the inflow to the system and intermediate delays in local stocks. Incomplete parameter knowledge is represented and propagated using Bayesian modeling. The method is implemented as a simulation framework in Python to support experts from different domains in the development of their application models. After the introduction of the method and its implementation, a case study is presented in which the framework is applied to predict the environmental concentrations of carbon nanotubes in Switzerland.

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“…In the context of reasoning with uncertainty, Bayesian networks (BN) is one of the most powerful tools that model (in)dependence relationships between variables [5]. A directed acyclic graph (DAG) is used to represent the variables as nodes and statistical dependencies between the variables as edges between the nodes.…”

Section: Introductionmentioning

confidence: 99%

ACO-Based Bayesian Network Ensembles for the Hierarchical Classification of Ageing-Related Proteins

Salama

Freitas

2013

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

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Abstract. The task of predicting protein functions using computational techniques is a major research area in the field of bioinformatics. Casting the task into a classification problem makes it challenging, since the classes (functions) to be predicted are hierarchically related, and a protein can have more than one function. One approach is to produce a set of local classifiers; each is responsible for discriminating between a subset of the classes in a certain level of the hierarchy. In this paper we tackle the hierarchical classification problem in a local fashion, by learning an ensemble of Bayesian network classifiers for each class in the hierarchy and combining their outputs with four alternative methods: a) selecting the best classifier, b) majority voting, c) weighted voting, and d) constructing a meta-classifier. The ensemble is built using ABC-Miner, our recently introduced Ant-based Bayesian Classification algorithm. We use different types of protein representations to learn different classification models. We empirically evaluate our proposed methods on an ageingrelated protein dataset created for this research.

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Learning Bayesian networks: approaches and issues

Cited by 271 publications

References 317 publications

Learning and prediction of relational time series

Learning and prediction of relational time series

A dynamic probabilistic material flow modeling method

ACO-Based Bayesian Network Ensembles for the Hierarchical Classification of Ageing-Related Proteins

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