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Essentials to Understand Probabilistic Graphical Models: A Tutorial about Inference and Learning

Christine Sinoquet

in Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Published in print:
2014
Published Online:
December 2014
ISBN:
9780198709022
eISBN:
9780191779619
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198709022.003.0002
Subject:
Mathematics, Probability / Statistics, Biostatistics

The aim of this chapter is to offer an advanced tutorial to scientists with no background or no deep background on probabilistic graphical models. To readers more familiar with these models, this ... More


Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Raphaël Mourad (ed.)

Published in print:
2014
Published Online:
December 2014
ISBN:
9780198709022
eISBN:
9780191779619
Item type:
book
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198709022.001.0001
Subject:
Mathematics, Probability / Statistics, Biostatistics

At the crossroads between statistics and machine learning, probabilistic graphical models provide a powerful formal framework to model complex data. Probabilistic graphical models are probabilistic ... More


Probabilistic Graphical Models for Next-generation Genomics and Genetics

Christine Sinoquet

in Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Published in print:
2014
Published Online:
December 2014
ISBN:
9780198709022
eISBN:
9780191779619
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198709022.003.0001
Subject:
Mathematics, Probability / Statistics, Biostatistics

The explosion in omics and other types of biological data has increased the demand for solid, large-scale statistical methods. These data can be discrete or continuous, dependent or independent, from ... More


A Taxonomy for Semi-Supervised Learning Methods

Seeger Matthias

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0002
Subject:
Computer Science, Machine Learning

This chapter proposes a simple taxonomy of probabilistic graphical models for the semi-supervised learning (SSL) problem. It provides some broad classes of algorithms for each of the families and ... More


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