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383,544
Exploiting Causal Independence in Bayesian Network Inference
 Journal of Artificial Intelligence Research
, 1996
"... A new method is proposed for exploiting causal independencies in exact Bayesian network inference. ..."
Abstract

Cited by 181 (10 self)
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A new method is proposed for exploiting causal independencies in exact Bayesian network inference.
A new look at causal independence
 In Proc. of the Tenth Conference on Uncertainty in Artificial Ingelligence
, 1994
"... Heckerman (1993) defined causal independence in terms of a set of temporal conditional independence statements. These statements formalized certain types of causal interaction where (1) the effect is independent of the order that causes are introduced and (2) the impact of a single cause on the effe ..."
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Cited by 79 (4 self)
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Heckerman (1993) defined causal independence in terms of a set of temporal conditional independence statements. These statements formalized certain types of causal interaction where (1) the effect is independent of the order that causes are introduced and (2) the impact of a single cause
On the impact of causal independence
, 1998
"... Reasoning in Bayesian networks is exponential in a graph parameter w3 known as induced width (also known as treewidth and maxclique size). In this paper, we investigate the potential of causal independence (CI) for improving this performance. We consider several tasks, such as belief updating, ndi ..."
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Cited by 14 (4 self)
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Reasoning in Bayesian networks is exponential in a graph parameter w3 known as induced width (also known as treewidth and maxclique size). In this paper, we investigate the potential of causal independence (CI) for improving this performance. We consider several tasks, such as belief updating
On the Impact of Causal Independence
, 1998
"... Reasoning in Bayesian networks is exponential in a graph parameter w 3 known as induced width (also known as treewidth and maxclique size). In this paper, we investigate the potential of causal independence (CI) for improving this performance. We consider several tasks, such as belief updating, ..."
Abstract
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Reasoning in Bayesian networks is exponential in a graph parameter w 3 known as induced width (also known as treewidth and maxclique size). In this paper, we investigate the potential of causal independence (CI) for improving this performance. We consider several tasks, such as belief updating
Symmetric causal independence models for classification
 In The third European Workshop on Probabilistic Graphical Models
, 2006
"... Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this paper, we propose an application of an extended class of causal independence models, causal independence models based on th ..."
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Cited by 2 (0 self)
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Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this paper, we propose an application of an extended class of causal independence models, causal independence models based
Inference with Causal Independence in the CPSC Network
 in: Proceedings of the 11th Conference on Uncertainty in ArtiĀ®cial Intelligence
, 1995
"... This paper reports experiments with the causal independence inference algorithm proposed by Zhang and Poole (1994b) on the CPSC network created by Pradhan et al (1994). It is found that the algorithm is able to answer 420 of the 422 possible zeroobservation queries, 94 of 100 randomly generate ..."
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Cited by 2 (0 self)
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This paper reports experiments with the causal independence inference algorithm proposed by Zhang and Poole (1994b) on the CPSC network created by Pradhan et al (1994). It is found that the algorithm is able to answer 420 of the 422 possible zeroobservation queries, 94 of 100 randomly
Causal independence for knowledge acquisition and inference. Also in this proceedings
, 1993
"... I introduce a temporal beliefnetwork representation of causal independence that a knowledge engineer can use to elicit probabilistic models. Like the current, atemporal beliefnetwork representation of causal independence, the new representation makes knowledge acquisition tractable. Unlike the ate ..."
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Cited by 49 (4 self)
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I introduce a temporal beliefnetwork representation of causal independence that a knowledge engineer can use to elicit probabilistic models. Like the current, atemporal beliefnetwork representation of causal independence, the new representation makes knowledge acquisition tractable. Unlike
EM Algorithm for Symmetric Causal Independence Models
"... Abstract. Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this paper, we present the EM algorithm to learn the parameters in causal independence models based on the symmetric B ..."
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Cited by 2 (1 self)
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Abstract. Causal independence modelling is a wellknown method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this paper, we present the EM algorithm to learn the parameters in causal independence models based on the symmetric
Parameter Estimation in Large Causal Independence Models
"... The assessment of a probability distribution that is associated with a Bayesian network is a challenging task, even if its topology is sparse. Special probability distributions, based on the notion of causal independence, have therefore been proposed, as these allow defining a probability distrib ..."
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Cited by 1 (1 self)
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The assessment of a probability distribution that is associated with a Bayesian network is a challenging task, even if its topology is sparse. Special probability distributions, based on the notion of causal independence, have therefore been proposed, as these allow defining a probability
Results 1  10
of
383,544