Expectation propagation

Known as: EP 
Expectation propagation (EP) is a technique in Bayesian machine learning. EP finds approximations to a probability distribution. It uses an iterative… (More)
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Highly Cited
2016
Highly Cited
2016
Deep Gaussian processes (DGPs) are multi-layer hierarchic l generalisations of Gaussian processes (GPs) and are formally… (More)
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2015
2015
Expectation propagation (EP) is a deterministic approximation algorithm that is often used to perform approximate Bayesian… (More)
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2008
2008
A series of corrections is developed for the fixed points of Ex pectation Propagation (EP), which is one of the most popular… (More)
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2005
2005
We present a general approximation method for Bayesian inference problems. The method is based on Expectation Propagation (EP… (More)
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Review
2005
Review
2005
This is a tutorial describing the Expectation Propagation (EP) algorithm for a general exponential family. Our focus is on… (More)
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Highly Cited
2005
Highly Cited
2005
We propose a novel framework for deriving approximations for intractable probabilistic models. This framework is based on a free… (More)
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Highly Cited
2004
Highly Cited
2004
In many real-world classification problems the input contains a large number of potentially irrelevant features. This paper… (More)
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Highly Cited
2002
Highly Cited
2002
  • Tom Heskes Onno Zoeter
  • 2002
We describe expectation propagation for approximate inference in dynamic Bayesian networks as a natural extension of Pearl's… (More)
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Highly Cited
2002
Highly Cited
2002
The generative aspect model is an extension of the multinomial model for text that allows word probabilities to vary… (More)
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Highly Cited
2001
Highly Cited
2001
This paper presents a new deterministic approximation technique in Bayesian networks. This method, “Expectation Propagation… (More)
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