At present, the researches on customer segmentation model based on Bayesian network are few. This paper makes a research on the classification problems based on Bayesian network. First of all, it used literature search and case study to describe the related knowledge and classification principles on Bayesian network. After that, combining with Adventure Works Cycles company's customer data, we ...

The problem of triangulation (decomposition) of Bayesian networks is considered. Triangularity of a Bayesian network is required in a general evidence propagation scheme on this network. Finding an optimal triangulation is NP-hard. A local search heuristic based on the idea of evolutionary algorithms is presented. The results obtained using existing and proposed approaches are compared on a bas...

In this paper we describe how to learn Bayesian networks from a summary of complete data in the form of a dependency network rather than from data directly. This method allows us to gain the advantages of both representations: scalable algorithms for learning dependency networks and convenient inference with Bayesian networks. Our approach is to use a dependency network as an “oracle” for the s...

In this paper we present a score following system based on a Dynamic Bayesian Network, using particle filtering as inference method. The proposed model sets itself apart from existing approaches by including two new extensions: A multi-level tempo model to improve alignment quality of performances with challenging tempo changes, and an extension to reflect different expressive characteristics o...

We consider the problem of reinforcement learning in factored-state MDPs in the setting in which learning is conducted in one long trial with no resets allowed. We show how to extend existing efficient algorithms that learn the conditional probability tables of dynamic Bayesian networks (DBNs) given their structure to the case in which DBN structure is not known in advance. Our method learns th...

Inference in Bayesian networks is known to be NP-hard, but if the network has bounded treewidth, then inference becomes tractable. Not surprisingly, learning networks that closely match the given data and have a bounded tree-width has recently attracted some attention. In this paper we aim to lay groundwork for future research on the topic by studying the exact complexity of this problem. We gi...

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, evenwhen 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 str...

We study the problem of learning Bayesian network structures from data. We develop an algorithm for finding the k-best Bayesian network structures. We propose to compute the posterior probabilities of hypotheses of interest by Bayesian model averaging over the k-best Bayesian networks. We present empirical results on structural discovery over several real and synthetic data sets and show that t...

Model-based diagnosis reasons backwards from a functional schematic of a system to isolate faults given observations of anoma­ lous behavior. We develop a fully proba­ bilistic approach to model based diagno­ sis and extend it to support hierarchical models. Our scheme translates the func­ tional schematic into a Bayesian network and diagnostic inference takes place in the Bayesian network. A B...