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A Guide to the Literature on Learning Probabilistic Networks From Data
, 1996
"... This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the ..."
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Cited by 179 (0 self)
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This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the different methodological communities, such as Bayesian, description length, and classical statistics. Basic concepts for learning and Bayesian networks are introduced and methods are then reviewed. Methods are discussed for learning parameters of a probabilistic network, for learning the structure, and for learning hidden variables. The presentation avoids formal definitions and theorems, as these are plentiful in the literature, and instead illustrates key concepts with simplified examples. Keywords Bayesian networks, graphical models, hidden variables, learning, learning structure, probabilistic networks, knowledge discovery. I. Introduction Probabilistic networks or probabilistic gra...
From association to causation via regression
 Indiana: University of Notre Dame
, 1997
"... For nearly a century, investigators in the social sciences have used regression models to deduce causeandeffect relationships from patterns of association. Path models and automated search procedures are more recent developments. In my view, this enterprise has not been successful. The models tend ..."
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Cited by 23 (7 self)
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For nearly a century, investigators in the social sciences have used regression models to deduce causeandeffect relationships from patterns of association. Path models and automated search procedures are more recent developments. In my view, this enterprise has not been successful. The models tend to neglect the difficulties in establishing causal relations, and the mathematical complexities tend to obscure rather than clarify the assumptions on which the analysis is based. Formal statistical inference is, by its nature, conditional. If maintained hypotheses A, B, C,... hold, then H can be tested against the data. However, if A, B, C,... remain in doubt, so must inferences about H. Careful scrutiny of maintained hypotheses should therefore be a critical part of empirical work a principle honored more often in the breach than the observance.
Learning Causal Networks from Data: A survey and a new algorithm for recovering possibilistic causal networks
, 1997
"... Introduction Reasoning in terms of cause and effect is a strategy that arises in many tasks. For example, diagnosis is usually defined as the task of finding the causes (illnesses) from the observed effects (symptoms). Similarly, prediction can be understood as the description of a future plausible ..."
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Cited by 19 (5 self)
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Introduction Reasoning in terms of cause and effect is a strategy that arises in many tasks. For example, diagnosis is usually defined as the task of finding the causes (illnesses) from the observed effects (symptoms). Similarly, prediction can be understood as the description of a future plausible situation where observed effects will be in accordance with the known causal structure of the phenomenon being studied. Causal models are a summary of the knowledge about a phenomenon expressed in terms of causation. Many areas of the ap # This work has been partially supported by the Spanish Comission Interministerial de Ciencia y Tecnologia Project CICYTTIC96 0878. plied sciences (econometry, biomedics, engineering, etc.) have used such a term to refer to models that yield explanations, allow for prediction and facilitate planning and decision making. Causal reasoning can be viewed as inference guided by a causation theory. That kind of inference can be further specialised into induc
Guest Editorial New perspectives on Causal Networks: the ®rst CaNew workshop
, 1998
"... www.elsevier.com/locate/ijar We are pleased to introduce a selection of the papers presented at the 1998 workshop on `Causal Networks from Inference to Data Mining', CaNew '98, [59]. This workshop was initiated from the feeling, shared by the organizers and cochairs, that the ®eld of Baye ..."
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www.elsevier.com/locate/ijar We are pleased to introduce a selection of the papers presented at the 1998 workshop on `Causal Networks from Inference to Data Mining', CaNew '98, [59]. This workshop was initiated from the feeling, shared by the organizers and cochairs, that the ®eld of Bayesian and, in general, Causal Networks deserved special attention from the international research community. We had a growing feeling that several areas had been neglected in research or deserved more attention. The common background of the editors and cochairs being in Machine Learning, we felt that some ideas that had been long been in use in Machine Learning had not been applied to Causal Networks. However, we also felt that other aspects dealing with the knowledge representation aspects of the Causal Network formalism were also of interest, namely, the construction of networks that used di€erent uncertainty formalisms, new inference methods and the relationship between the classical interpretation of Causal Network and the new ones. The rest of the Workshop Programme Committee members had a similar feeling about that and we tried to convey this by introducing in the workshop title both ends of the Causal Networks research spectrum: from inference to Data Mining. We comment in more detail in Section 3 the opportunities that, from our point of view, lay hidden between both.