نتایج جستجو برای: bayesian causal mapbcm

تعداد نتایج: 142773  

Journal: :CoRR 2017
Ahmed M. Alaa Mihaela van der Schaar

We investigate the problem of estimating the causal effect of a treatment on individual subjects from observational data; this is a central problem in various application domains, including healthcare, social sciences, and online advertising. Within the Neyman-Rubin potential outcomes model, we use the Kullback-Leibler (KL) divergence between the estimated and true distributions as a measure of...

Journal: :Int. J. Intell. Syst. 2015
Peter J. F. Lucas Arjen Hommersom

The theory of causal independence is frequently used to facilitate the assessment of the probabilistic parameters of discrete probability distributions of complex Bayesian networks. Although it is possible to include continuous parameters in Bayesian networks as well, such parameters could not, so far, be modeled by means of causal-independence theory, as a theory of continuous causal independe...

2015
Pascal Caillet Sarah Klemm Michel Ducher Alexandre Aussem Anne-Marie Schott

OBJECTIVES Hip fractures commonly result in permanent disability, institutionalization or death in elderly. Existing hip-fracture predicting tools are underused in clinical practice, partly due to their lack of intuitive interpretation. By use of a graphical layer, Bayesian network models could increase the attractiveness of fracture prediction tools. Our aim was to study the potential contribu...

2014
Mattias Tiger Fredrik Heintz

We propose an unsupervised stream processing framework that learns a Bayesian representation of observed spatio-temporal activities and their causal relations. The dynamics of the activities are modeled using sparse Gaussian processes and their causal relations using a causal Bayesian graph. This allows the model to be efficient through compactness and sparsity in the causal graph, and to provi...

2010
Hee Seung Lee

We recently proposed a theoretical integration of analogical transfer with causal learning and inference (Lee & Holyoak, 2008). A Bayesian theory of learning and inference based on causal models (Lee, Holyoak & Lu, 2009) accounts for the fact that judgments of confidence in analogical inferences are partially dissociable from measures of the quality of the mapping between source and target anal...

Journal: :Journal of the American Medical Informatics Association : JAMIA 2015
Gregory F. Cooper Ivet Bahar Michael J. Becich Panayiotis V. Benos Jeremy M. Berg Jeremy U. Espino Clark Glymour Rebecca Crowley Jacobson Michelle Kienholz Adrian V. Lee Xinghua Lu Richard Scheines

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesi...

Journal: :Pattern Recognition Letters 2015
Pedro A. Ortega

Bayesian probability theory is one of the most successful frameworks to model reasoning under uncertainty. Its defining property is the interpretation of probabilities as degrees of belief in propositions about the state of the world relative to an inquiring subject. This essay examines the notion of subjectivity by drawing parallels between Lacanian theory and Bayesian probability theory, and ...

2014
Rafael Chaves Lukas Luft Thiago O. Maciel David Gross Dominik Janzing Bernhard Schölkopf

One of the goals of probabilistic inference is to decide whether an empirically observed distribution is compatible with a candidate Bayesian network. However, Bayesian networks with hidden variables give rise to highly non-trivial constraints on the observed distribution. Here, we propose an information-theoretic approach, based on the insight that conditions on entropies of Bayesian networks ...

Journal: :Cognitive psychology 2015
Saiwing Yeung Thomas L Griffiths

When we try to identify causal relationships, how strong do we expect that relationship to be? Bayesian models of causal induction rely on assumptions regarding people's a priori beliefs about causal systems, with recent research focusing on people's expectations about the strength of causes. These expectations are expressed in terms of prior probability distributions. While proposals about the...

2006
Rasa Jurgelenaite Tom Heskes

Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. Many Bayesian network models incorporate causal independence assumptions; however, only the noisy OR and noisy AND, two examples of causal independence models, are used in practice. Their underlying assumption that either at lea...

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