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3,336
On Evidence Absorption for Belief Networks
, 1996
"... More and more reallife applications of the beliefnetwork framework are emerging. As applications grow larger, the belief networks involved increase in size accordingly. For large belief networks, probabilistic inference tends to become rather timeconsuming. In the worst case this tendency may not ..."
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Cited by 1 (0 self)
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More and more reallife applications of the beliefnetwork framework are emerging. As applications grow larger, the belief networks involved increase in size accordingly. For large belief networks, probabilistic inference tends to become rather timeconsuming. In the worst case this tendency may
Inference in belief networks: A procedural guide
 International Journal of Approximate Reasoning
, 1996
"... Belief networks are popular tools for encoding uncertainty in expert systems. These networks rely on inference algorithms to compute beliefs in the context of observed evidence. One established method for exact inference onbelief networks is the Probability Propagation in Trees of Clusters (PPTC) al ..."
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Cited by 176 (5 self)
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Belief networks are popular tools for encoding uncertainty in expert systems. These networks rely on inference algorithms to compute beliefs in the context of observed evidence. One established method for exact inference onbelief networks is the Probability Propagation in Trees of Clusters (PPTC
Causality in Bayesian Belief Networks
 In Proceedings of the Ninth Annual Conference on Uncertainty in Artificial Intelligence (UAI93
, 1993
"... We address the problem of causal interpretation of the graphical structure of Bayesian belief networks (BBNs). We review the concept of causality explicated in the domain of structural equations models and show that it is applicable to BBNs. In this view, which we call mechanismbased, causality is ..."
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Cited by 52 (19 self)
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We address the problem of causal interpretation of the graphical structure of Bayesian belief networks (BBNs). We review the concept of causality explicated in the domain of structural equations models and show that it is applicable to BBNs. In this view, which we call mechanismbased, causality
Ideal Reformulation of Belief Networks
"... The intelligent reformulation or restructuring of a belief network can greatly increase the efficiency of inference. However, time expended for reformulation is not available for performing inference. Thus, under time pressure, there is a tradeoff between the time dedicated to reformulating the netw ..."
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Cited by 31 (6 self)
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The intelligent reformulation or restructuring of a belief network can greatly increase the efficiency of inference. However, time expended for reformulation is not available for performing inference. Thus, under time pressure, there is a tradeoff between the time dedicated to reformulating
Learning Bayesian belief networks: An approach based on the MDL principle
 Computational Intelligence
, 1994
"... A new approach for learning Bayesian belief networks from raw data is presented. The approach is based on Rissanen's Minimal Description Length (MDL) principle, which is particularly well suited for this task. Our approach does not require any prior assumptions about the distribution being lear ..."
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Cited by 254 (7 self)
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A new approach for learning Bayesian belief networks from raw data is presented. The approach is based on Rissanen's Minimal Description Length (MDL) principle, which is particularly well suited for this task. Our approach does not require any prior assumptions about the distribution being
Multiresolution Deep Belief Networks
"... Motivated by the observation that coarse and fine resolutions of an image reveal different structures in the underlying visual phenomenon, we present a model based on the Deep Belief Network (DBN) which learns features from the multiscale representation of images. A Laplacian Pyramid is first constr ..."
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Cited by 3 (0 self)
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Motivated by the observation that coarse and fine resolutions of an image reveal different structures in the underlying visual phenomenon, we present a model based on the Deep Belief Network (DBN) which learns features from the multiscale representation of images. A Laplacian Pyramid is first
Belief Networks In Construction Simulation
, 1998
"... A method for automatically improving the performance of construction operations was developed by the integration of computer simulation and belief networks. The simulation model is used to represent the operation and to determine the effect that changes in resource configuration have on the model pe ..."
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Cited by 1 (0 self)
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A method for automatically improving the performance of construction operations was developed by the integration of computer simulation and belief networks. The simulation model is used to represent the operation and to determine the effect that changes in resource configuration have on the model
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
Parallel Learning of Belief Networks
 in Large and Difficult Domains,” Data Mining and Knowledge Discovery
, 1999
"... Learning belief networks from a large dataset over a large domain can be computationally expensive even with a singlelink lookahead search. It has been shown that a class of probabilistic domain models cannot be learned correctly by several existing algorithms which employ a singlelink lookahead s ..."
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Cited by 9 (0 self)
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Learning belief networks from a large dataset over a large domain can be computationally expensive even with a singlelink lookahead search. It has been shown that a class of probabilistic domain models cannot be learned correctly by several existing algorithms which employ a singlelink lookahead
A fast learning algorithm for deep belief nets
 Neural Computation
, 2006
"... We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in denselyconnected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a ..."
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Cited by 970 (49 self)
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We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in denselyconnected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer
Results 11  20
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3,336