# Expectation propagation

## Papers overview

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Highly Cited

2016

Highly Cited

2016

- ICML
- 2016

Deep Gaussian processes (DGPs) are multi-layer hierarchic l generalisations of Gaussian processes (GPs) and are formallyâ€¦Â (More)

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2015

2015

- NIPS
- 2015

Expectation propagation (EP) is a deterministic approximation algorithm that is often used to perform approximate Bayesianâ€¦Â (More)

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2008

2008

- NIPS
- 2008

A series of corrections is developed for the fixed points of Ex pectation Propagation (EP), which is one of the most popularâ€¦Â (More)

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2005

2005

- AISTATS
- 2005

We present a general approximation method for Bayesian inference problems. The method is based on Expectation Propagation (EPâ€¦Â (More)

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Review

2005

Review

2005

- 2005

This is a tutorial describing the Expectation Propagation (EP) algorithm for a general exponential family. Our focus is onâ€¦Â (More)

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Highly Cited

2005

Highly Cited

2005

- Journal of Machine Learning Research
- 2005

We propose a novel framework for deriving approximations for intractable probabilistic models. This framework is based on a freeâ€¦Â (More)

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Highly Cited

2004

Highly Cited

2004

- ICML
- 2004

In many real-world classification problems the input contains a large number of potentially irrelevant features. This paperâ€¦Â (More)

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Highly Cited

2002

Highly Cited

2002

- 2002

We describe expectation propagation for approximate inference in dynamic Bayesian networks as a natural extension of Pearl'sâ€¦Â (More)

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Highly Cited

2002

Highly Cited

2002

- UAI
- 2002

The generative aspect model is an extension of the multinomial model for text that allows word probabilities to varyâ€¦Â (More)

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Highly Cited

2001

Highly Cited

2001

- UAI
- 2001

This paper presents a new deterministic approximation technique in Bayesian networks. This method, â€œExpectation Propagationâ€¦Â (More)

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