Identifying independence in bayesian networks

Geiger, Dan; Verma, Thomas; Pearl, Judea

doi:10.1002/net.3230200504

Cited by 385 publications

(279 citation statements)

References 15 publications

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“…It can be shown that if sets of variables X and Z are d-separated by Y in a directed acyclic graph G, then X is independent of Z conditional on Y in every distribution compatible with G [24,52]. It is precisely this property that will be exploited in the algorithm of Cheng to learn the Bayesian network structure.…”

Section: General Bayesian Network Classifiersmentioning

confidence: 99%

Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

Baesens

Verstraeten

Poel

et al. 2004

European Journal of Operational Research

118

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Undoubtedly, customer relationship management has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing literature have shown that large differences exist within the group of long-life customers in terms of spending and spending evolution. Therefore, this paper focuses on introducing a measure of a customerÕs future spending evolution that might improve relationship marketing decision making. In this study, from a marketing point of view, we focus on predicting whether a newly acquired customer will increase or decrease his/her future spending from initial purchase information. This is essentially a classification task. The main contribution of this study lies in comparing and evaluating several Bayesian network classifiers with statistical and other artificial intelligence techniques for the purpose of classifying customers in the binary classification problem at hand. Certain Bayesian network classifiers have been recently proposed in the artificial intelligence literature as probabilistic white-box classifiers which allow to give a clear insight into the relationships between the variables of the domain under study. We discuss and evaluate several types of Bayesian network classifiers and their corresponding structure learning algorithms. We contribute to the literature by providing experimental evidence that: (1) Bayesian network classifiers offer an interesting and viable alternative for our customer lifecycle slope estimation problem; (2) the Markov Blanket concept allows for a natural form of attribute selection that was very effective for the application at hand; (3) the sign of the slope can be predicted with a powerful and parsimonious general, unrestricted Bayesian network classifier; (4) a set of three variables measuring the volume of initial purchases and the degree to which customers originally buy in different categories, are powerful predictors for estimating the sign of the slope, and might therefore provide desirable additional information for relationship marketing decision making.

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Section: General Bayesian Network Classifiersmentioning

confidence: 99%

Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

Baesens

Verstraeten

Poel

et al. 2004

European Journal of Operational Research

118

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“…In the case of a directed acyclic graph they can be read from the graph using a graph theoretic criterion called d-separation [35,24]. What is to be understood by conditional independence depends on the uncertainty calculus the graphical model is based on.…”

Section: Graphical Modelsmentioning

confidence: 99%

Possibilistic Graphical Models

Borgelt

Gebhardt

Kruse

2000

Computational Intelligence in Data Mining

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Graphical modeling is an important method to efficiently represent and analyze uncertain information in knowledge-based systems. Its most prominent representatives are Bayesian networks and Markov networks for probabilistic reasoning, which have been well-known for over ten years now. However, they suffer from certain deficiencies, if imprecise information has to be taken into account. Therefore possibilistic graphical modeling has recently emerged as a promising new area of research. Possibilistic networks are a noteworthy alternative to probabilistic networks whenever it is necessary to model both uncertainty and imprecision. Imprecision, understood as set-valued data, has often to be considered in situations in which information is obtained from human observers or imprecise measuring instruments. In this paper we provide an overview on the state of the art of possibilistic networks w.r.t. to propagation and learning algorithms.

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“…A causal directed acyclic graph defined by nonparametric structural equations satisfies the global Markov property as stated above (cf. Verma & Pearl, 1988;Geiger et al, 1990;Lauritzen et al, 1990;Pearl, 2000). We will say that (V 1 , .…”

Section: Causal Directed Acyclic Graphs and Signed Edgesmentioning

confidence: 99%

Directed acyclic graphs with edge-specific bounds

VanderWeele¹,

Tan²

2011

Biometrika

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SUMMARYWe give a definition of a bounded edge within the causal directed acyclic graph framework. A bounded edge generalizes the notion of a signed edge and is defined in terms of bounds on a ratio of survivor probabilities. We derive rules concerning the propagation of bounds. Bounds on causal effects in the presence of unmeasured confounding are also derived using bounds related to specific edges on a graph. We illustrate the theory developed by an example concerning estimating the effect of antihistamine treatment on asthma in the presence of unmeasured confounding.

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Identifying independence in bayesian networks

Cited by 385 publications

References 15 publications

Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

Possibilistic Graphical Models

Directed acyclic graphs with edge-specific bounds

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