We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the Hilbert-Schmidt norm of kernel-based cross-covariance operators. Following the line of the common faithfulness assumption of constraint-based 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 constraint-based PC algorithm. 1.

bayesian network classifier

We propose the recursive autonomy identification (RAI) algorithm for constraint-based (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 sub-structures. The sequence of operations is performed recursively for each autonomous substructure while simultaneously increasing the order of the CI test. While other CB algorithms d-separate 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 run-time of the algorithm and increases the accuracy by diminishing the curse-of-dimensionality. When the RAI algorithm learned structures from databases representing synthetic problems, known networks and natural problems, it demonstrated superiority with respect to computational complexity, run-time, structural correctness and classification accuracy over the

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 higher-level events. As experimental results verify, pcet simplifies the development process and boosts the efficiency of any system dealing with inherently uncertain data streams. 1

Research article Predicting the effect of missense mutations on protein function: analysis with Bayesian networks

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 fixed-structure 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.

We propose a constraint-based 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 d-separating 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 run-time as well as increases accuracy since diminishing the curse-of-dimensionality. When evaluated on synthetic and "real-world " databases as well as the ALARM network, the RAI algorithm shows better structural correctness, run-time reduction along with accuracy improvement compared to popular constraint-based structure learning algorithms. Accuracy improvement is also demonstrated when compared to a common search-and-score structure learning algorithm. 1

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Employing fuzzy intervals and loop-based methodology for designing structural signature: an application to symbol recognition

We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational graph, which is used for computing a feature vector for the symbol. This signature corresponds to geometry and topology of the symbol. We learn a Bayesian network to encode joint probability distribution of symbol signatures and use it in a supervised learning scenario for graphic symbol recognition. We have evaluated our method on synthetically deformed and degraded images of presegmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates. 1. Introduction and