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Foundations for Bayesian networks
, 2001
"... Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions, or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probabi ..."
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Bayesian networks are normally given one of two types of foundations: they are either treated purely formally as an abstract way of representing probability functions, or they are interpreted, with some causal interpretation given to the graph in a network and some standard interpretation of probability given to the probabilities specified in the network. In this chapter I argue that current foundations are problematic, and put forward new foundations which involve aspects of both the interpreted and the formal approaches. One standard approach is to interpret a Bayesian network objectively: the graph in a Bayesian network represents causality in the world and the specified probabilities are objective, empirical probabilities. Such an interpretation founders when the Bayesian network independence assumption (often called the causal Markov condition) fails to hold. In §2 I catalogue the occasions when the independence assumption fails, and show that such failures are pervasive. Next, in §3, I show that even where the independence assumption does hold objectively, an agent’s causal knowledge is unlikely to satisfy the assumption with respect to her subjective probabilities, and that slight differences between an agent’s subjective Bayesian network and an objective Bayesian network can lead to large differences between probability distributions determined by these networks. To overcome these difficulties I put forward logical Bayesian foundations in §5. I show that if the graph and probability specification in a Bayesian network are thought of as an agent’s background knowledge, then the agent is most rational if she adopts the probability distribution determined by the
Understanding of what engineers “do
 LSE Centre for Natural and Social Sciences, www.lse.ac.uk/Depts/cpnss/proj_causality.htm
, 2002
"... presented at ..."
A Comparison of Association Rule Discovery and Bayesian Network Causal Inference Algorithms to Discover Relationships in Discrete Data
 in Discrete Data, Proceedings of the Thirteenth Canadian Artificial Intelligence Conference (AI'2000
"... . Association rules discovered through attributeoriented induction are commonly used in data mining tools to express relationships between variables. However, causal inference algorithms discover more concise relationships between variables, namely, relations of direct cause . These algorithms p ..."
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. Association rules discovered through attributeoriented induction are commonly used in data mining tools to express relationships between variables. However, causal inference algorithms discover more concise relationships between variables, namely, relations of direct cause . These algorithms produce regressive structured equation models for continuous linear data and Bayes networks for discrete data. This work compares the effectiveness of causal inference algorithms with association rule induction for discovering patterns in discrete data. 1 Introduction Association rules discovered using attributeoriented induction in tools such as DBMiner are used to express relationships among variables. However, causal inference algorithms discover deeper relationships, namely a variety of causal relationships including genuine causality, potential causality and spurious association [7,8]. In this paper, we describe and compare association rule generation based on their implementation...
* IPEA/Directorate of Rio de Janeiro. * * IPEA/Directorate of Rio de Janeiro and UERJ. Governo Federal
, 2005
"... Uma publicação que tem o objetivo de divulgar resultados de estudos desenvolvidos, direta ou indiretamente, pelo IPEA e trabalhos que, por sua relevância, levam informações para profissionais especializados e estabelecem um espaço para sugestões. Fundação pública vinculada ao Ministério do Planejame ..."
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Uma publicação que tem o objetivo de divulgar resultados de estudos desenvolvidos, direta ou indiretamente, pelo IPEA e trabalhos que, por sua relevância, levam informações para profissionais especializados e estabelecem um espaço para sugestões. Fundação pública vinculada ao Ministério do Planejamento, Orçamento e Gestão, o IPEA fornece suporte técnico e institucional às ações governamentais, possibilitando a formulação de inúmeras políticas públicas e programas de desenvolvimento brasileiro, e disponibiliza, para a sociedade, pesquisas e estudos realizados por seus técnicos.
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"... Abstract. A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, ..."
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Abstract. A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention. Three, because the model has both a causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in causal form) and data. Four, Bayesian statistical methods in conjunction with Bayesian networks offer an efficient and principled approach for avoiding the overfitting of data. In this paper, we discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models. With regard to the latter task, we describe methods for learning both the parameters and structure of a Bayesian network, including techniques for learning with incomplete data. In addition, we relate