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A Bayesian method for the induction of probabilistic networks from data
 MACHINE LEARNING
, 1992
"... This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computerassisted hypothesis testing, automated scientific discovery, and automated construction of probabili ..."
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Cited by 1400 (31 self)
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This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computerassisted hypothesis testing, automated scientific discovery, and automated construction
The neural basis of human error processing: Reinforcement learning, dopamine, and the errorrelated negativity
 PSYCHOLOGICAL REVIEW 109:679–709
, 2002
"... The authors present a unified account of 2 neural systems concerned with the development and expression of adaptive behaviors: a mesencephalic dopamine system for reinforcement learning and a “generic ” errorprocessing system associated with the anterior cingulate cortex. The existence of the error ..."
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Cited by 430 (20 self)
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of the errorprocessing system has been inferred from the errorrelated negativity (ERN), a component of the eventrelated brain potential elicited when human participants commit errors in reactiontime tasks. The authors propose that the ERN is generated when a negative reinforcement learning signal
Discovery of Inference Rules for Question Answering
 Natural Language Engineering
, 2001
"... One of the main challenges in questionanswering is the potential mismatch between the expressions in questions and the expressions in texts. While humans appear to use inference rules such as “X writes Y ” implies “X is the author of Y ” in answering questions, such rules are generally unavailable ..."
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Cited by 309 (7 self)
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One of the main challenges in questionanswering is the potential mismatch between the expressions in questions and the expressions in texts. While humans appear to use inference rules such as “X writes Y ” implies “X is the author of Y ” in answering questions, such rules are generally unavailable
Hierarchical Bayesian Inference in the Visual Cortex
, 2002
"... this paper, we propose a Bayesian theory of hierarchical cortical computation based both on (a) the mathematical and computational ideas of computer vision and pattern the ory and on (b) recent neurophysiological experimental evidence. We ,2 have proposed that Grenander's pattern theory 3 coul ..."
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Cited by 300 (2 self)
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, however, was rather limited, dealing only with binary images. Moreover, its feedback mechanisms were engaged only during the learning of the feedforward connections but not during perceptual inference, though the Gibbs sampling process for inference can potentially be interpreted as topdown feedback
RaoBlackwellised Particle Filtering for Dynamic Bayesian Networks
"... Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of probability distribution, nonlinearity and nonstationarity. They have appeared in several fields under such names as “conde ..."
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Cited by 348 (11 self)
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Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of probability distribution, nonlinearity and nonstationarity. They have appeared in several fields under such names
Robust Higher Order Potentials for Enforcing Label Consistency
, 2009
"... This paper proposes a novel framework for labelling problems which is able to combine multiple segmentations in a principled manner. Our method is based on higher order conditional random fields and uses potentials defined on sets of pixels (image segments) generated using unsupervised segmentation ..."
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Cited by 259 (34 self)
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n Potts model recently proposed by Kohli et al. We prove that the optimal swap and expansion moves for energy functions composed of these potentials can be computed by solving a stmincut problem. This enables the use of powerful graph cut based move making algorithms for performing inference
Fields of experts: A framework for learning image priors
 In CVPR
, 2005
"... We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The approach extends traditional Markov Random Field (MRF) models by learning potential functions over extended pixel neighborhood ..."
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Cited by 292 (4 self)
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We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The approach extends traditional Markov Random Field (MRF) models by learning potential functions over extended pixel
Dependency networks for inference, collaborative filtering, and data visualization
 Journal of Machine Learning Research
"... We describe a graphical model for probabilistic relationshipsan alternative tothe Bayesian networkcalled a dependency network. The graph of a dependency network, unlike aBayesian network, is potentially cyclic. The probability component of a dependency network, like aBayesian network, is a set of ..."
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Cited by 208 (12 self)
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We describe a graphical model for probabilistic relationshipsan alternative tothe Bayesian networkcalled a dependency network. The graph of a dependency network, unlike aBayesian network, is potentially cyclic. The probability component of a dependency network, like aBayesian network, is a set
Word Learning as Bayesian Inference
 In Proceedings of the 22nd Annual Conference of the Cognitive Science Society
, 2000
"... The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word’s referents, by making rational inductive inferences that integrate pr ..."
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Cited by 175 (33 self)
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The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word’s referents, by making rational inductive inferences that integrate
THE VOCABULARY OF BRAIN POTENTIALS: INFERRING COGNITIVE EVENTS FROM BRAIN POTENTIALS IN OPERATIONAL SETTINGS
, 1976
"... in i ■ ' ^ r lll^lWllll^MilMiMWMtl^ • Ur ..."
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