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A Kernelbased Causal Learning Algorithm
"... We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the HilbertSchmidt norm of kernelbased crosscovariance operators. Following the line of the common faithfulness assumption of constraintbased causal learning, our approach assumes th ..."
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We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the HilbertSchmidt norm of kernelbased crosscovariance operators. Following the line of the common faithfulness assumption of constraintbased causal learning, our approach assumes that a variable Z is likely to be a common effect of X and Y, if conditioning on Z increases the dependence between X and Y. Based on this assumption, we collect “votes” for hypothetical causal directions and orient the edges by the majority principle. In most experiments with known causal structures, our method provided plausible results and outperformed the conventional constraintbased PC algorithm. 1.
Bayesian Network Structure Learning by Recursive Autonomy Identification Raanan Yehezkel ∗ Video Analytics Group
"... We propose the recursive autonomy identification (RAI) algorithm for constraintbased (CB) Bayesian network structure learning. The RAI algorithm learns the structure by sequential application of conditional independence (CI) tests, edge direction and structure decomposition into autonomous substru ..."
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We propose the recursive autonomy identification (RAI) algorithm for constraintbased (CB) Bayesian network structure learning. The RAI algorithm learns the structure by sequential application of conditional independence (CI) tests, edge direction and structure decomposition into autonomous substructures. The sequence of operations is performed recursively for each autonomous substructure while simultaneously increasing the order of the CI test. While other CB algorithms dseparate structures and then direct the resulted undirected graph, the RAI algorithm combines the two processes from the outset and along the procedure. By this means and due to structure decomposition, learning a structure using RAI requires a smaller number of CI tests of high orders. This reduces the complexity and runtime of the algorithm and increases the accuracy by diminishing the curseofdimensionality. When the RAI algorithm learned structures from databases representing synthetic problems, known networks and natural problems, it demonstrated superiority with respect to computational complexity, runtime, structural correctness and classification accuracy over the
Learning Ground CPlogic Theories by means of Bayesian Network Techniques
"... Abstract. Causal relationships are present in many application domains. CPlogic is a probabilistic modeling language that is especially designed to express such relationships. This paper investigates the learning of CPtheories from examples, and focusses on structure learning. The proposed approac ..."
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Abstract. Causal relationships are present in many application domains. CPlogic is a probabilistic modeling language that is especially designed to express such relationships. This paper investigates the learning of CPtheories from examples, and focusses on structure learning. The proposed approach is based on a transformation between CPlogic theories and Bayesian networks, that is, the method applies Bayesian network learning techniques to learn a CPtheory in the form of an equivalent Bayesian network. We propose a constrained refinement operator for such networks that guarantees equivalence to a valid CPtheory. We experimentally compare our method to a standard method for learning Bayesian networks. This shows that CPtheories can be learned more efficiently than Bayesian networks given that causal relationships are present in the domain. 1
Probabilistic Complex Event Triggering
"... Abstract. Recently, wireless sensor devices have been widely deployed in various application settings (including environmental research, control systems, etc.). Because of the inherent unreliability of sensor readings, any kind of reasoning in sensor environments needs to carefully account for noise ..."
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Abstract. Recently, wireless sensor devices have been widely deployed in various application settings (including environmental research, control systems, etc.). Because of the inherent unreliability of sensor readings, any kind of reasoning in sensor environments needs to carefully account for noise. The key goal of pcet is to build an infrastructure that can automatically infer and reason about the probabilities of triggered events, using a principled probabilistic model for the underlying sensor data. Through such probabilistic reasoning, pcet can incorporate uncertainly factors and make finer – grain decisions on event occurrences. This is achieved through the use of a Bayesian Network to directly model and exploit correlations across different sensors and the definition of a complex – event language, which allows users / applications to create hierarchies of higherlevel events. As experimental results verify, pcet simplifies the development process and boosts the efficiency of any system dealing with inherently uncertain data streams. 1
BMC Bioinformatics BioMed Central
, 2006
"... Research article Predicting the effect of missense mutations on protein function: analysis with Bayesian networks ..."
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Research article Predicting the effect of missense mutations on protein function: analysis with Bayesian networks
BAYESIAN NETWORK STRUCTURAL LEARNING AND INCOMPLETE DATA
"... The Bayesian network formalism is becoming increasingly popular in many areas such as decision aid, diagnosis and complex systems control, in particular thanks to its inference capabilities, even when data are incomplete. Besides, estimating the parameters of a fixedstructure Bayesian network is ea ..."
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The Bayesian network formalism is becoming increasingly popular in many areas such as decision aid, diagnosis and complex systems control, in particular thanks to its inference capabilities, even when data are incomplete. Besides, estimating the parameters of a fixedstructure Bayesian network is easy. However, very few methods are capable of using incomplete cases as a base to determine the structure of a Bayesian network. In this paper, we take up the structural EM algorithm principle [9, 10] to propose an algorithm which extends the Maximum Weight Spanning Tree algorithm to deal with incomplete data. We also propose to use this extension in order to (1) speed up the structural EM algorithm or (2) in classification tasks extend the Tree Augmented Naive classifier in order to deal with incomplete data. 1.
Recursive Autonomy Identification for Bayesian Network Structure Learning
"... We propose a constraintbased algorithm for Bayesian network structure learning called recursive autonomy identification (RAI). The RAI algorithm learns the structure by recursive application of conditional independence (CI) tests of increasing orders, edge direction and structure decomposition into ..."
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We propose a constraintbased algorithm for Bayesian network structure learning called recursive autonomy identification (RAI). The RAI algorithm learns the structure by recursive application of conditional independence (CI) tests of increasing orders, edge direction and structure decomposition into autonomous substructures. In comparison to other constraintbased algorithms dseparating structures and then directing the resulted undirected graph, the RAI algorithm combines the two processes from the outset and along the procedure. Learning using the RAI algorithm renders smaller condition sets thus requires a smaller number of high order CI tests. This reduces complexity and runtime as well as increases accuracy since diminishing the curseofdimensionality. When evaluated on synthetic and "realworld " databases as well as the ALARM network, the RAI algorithm shows better structural correctness, runtime reduction along with accuracy improvement compared to popular constraintbased structure learning algorithms. Accuracy improvement is also demonstrated when compared to a common searchandscore structure learning algorithm. 1
permission. Probabilistic Complex Event Triggering
"... personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires pri ..."
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personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific
22
"... Employing fuzzy intervals and loopbased methodology for designing structural signature: an application to symbol recognition ..."
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Employing fuzzy intervals and loopbased methodology for designing structural signature: an application to symbol recognition