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# Variational Inference Guide

Neil D. Lawrence , 2002.

#### Abstract

This report is a brief introduction to variational inference for Bayesian models from the perspective of the Expectation Maximisation (EM) algorithm @Dempster:EM77. We start with an overview of the EM algorithm from the perspective of variational inference and then we show how approximate inference may also be performed. We discuss briefly when variational inference may be used and finally we mention the variational importance sampler as an alternative approach.

#### Cite this Paper

BibTeX

```
@InProceedings{pmlr-v-lawrence-variationalguide02,
title = {Variational Inference Guide},
author = {Neil D. Lawrence},
year = {},
editor = {},
url = {http://inverseprobability.com/publications/lawrence-variationalguide02.html},
abstract = {This report is a brief introduction to variational inference for Bayesian models from the perspective of the Expectation Maximisation (EM) algorithm @Dempster:EM77. We start with an overview of the EM algorithm from the perspective of variational inference and then we show how approximate inference may also be performed. We discuss briefly when variational inference may be used and finally we mention the variational importance sampler as an alternative approach.}
}
```

Endnote

```
%0 Conference Paper
%T Variational Inference Guide
%A Neil D. Lawrence
%B
%C Proceedings of Machine Learning Research
%D
%E
%F pmlr-v-lawrence-variationalguide02
%I PMLR
%J Proceedings of Machine Learning Research
%P --
%U http://inverseprobability.com
%V
%W PMLR
%X This report is a brief introduction to variational inference for Bayesian models from the perspective of the Expectation Maximisation (EM) algorithm @Dempster:EM77. We start with an overview of the EM algorithm from the perspective of variational inference and then we show how approximate inference may also be performed. We discuss briefly when variational inference may be used and finally we mention the variational importance sampler as an alternative approach.
```

RIS

```
TY - CPAPER
TI - Variational Inference Guide
AU - Neil D. Lawrence
BT -
PY -
DA -
ED -
ID - pmlr-v-lawrence-variationalguide02
PB - PMLR
SP -
DP - PMLR
EP -
L1 -
UR - http://inverseprobability.com/publications/lawrence-variationalguide02.html
AB - This report is a brief introduction to variational inference for Bayesian models from the perspective of the Expectation Maximisation (EM) algorithm @Dempster:EM77. We start with an overview of the EM algorithm from the perspective of variational inference and then we show how approximate inference may also be performed. We discuss briefly when variational inference may be used and finally we mention the variational importance sampler as an alternative approach.
ER -
```

APA

`Lawrence, N.D.. (). Variational Inference Guide. `*, in PMLR* :-

#### Related Material

BibTeX

```
@InProceedings{/lawrence-variationalguide02,
title = {Variational Inference Guide},
author = {Neil D. Lawrence},
year = {},
editor = {},
url = {http://inverseprobability.com/publications/lawrence-variationalguide02.html},
abstract = {This report is a brief introduction to variational inference for Bayesian models from the perspective of the Expectation Maximisation (EM) algorithm @Dempster:EM77. We start with an overview of the EM algorithm from the perspective of variational inference and then we show how approximate inference may also be performed. We discuss briefly when variational inference may be used and finally we mention the variational importance sampler as an alternative approach.}
}
```

Endnote

```
%0 Conference Paper
%T Variational Inference Guide
%A Neil D. Lawrence
%B
%C Proceedings of Machine Learning Research
%D
%E
%F /lawrence-variationalguide02
%I PMLR
%J Proceedings of Machine Learning Research
%P --
%U http://inverseprobability.com
%V
%W PMLR
%X This report is a brief introduction to variational inference for Bayesian models from the perspective of the Expectation Maximisation (EM) algorithm @Dempster:EM77. We start with an overview of the EM algorithm from the perspective of variational inference and then we show how approximate inference may also be performed. We discuss briefly when variational inference may be used and finally we mention the variational importance sampler as an alternative approach.
```

RIS

```
TY - CPAPER
TI - Variational Inference Guide
AU - Neil D. Lawrence
BT -
PY -
DA -
ED -
ID - /lawrence-variationalguide02
PB - PMLR
SP -
DP - PMLR
EP -
L1 -
UR - http://inverseprobability.com/publications/lawrence-variationalguide02.html
AB - This report is a brief introduction to variational inference for Bayesian models from the perspective of the Expectation Maximisation (EM) algorithm @Dempster:EM77. We start with an overview of the EM algorithm from the perspective of variational inference and then we show how approximate inference may also be performed. We discuss briefly when variational inference may be used and finally we mention the variational importance sampler as an alternative approach.
ER -
```

APA

`Lawrence, N.D.. (). Variational Inference Guide. `*, in PMLR* :-