Probabilistic Graphical Models for Genetics Genomics and Postgenomics

This book PDF is perfect for those who love Mathematics genre, written by Christine Sinoquet and published by Oxford University Press, USA which was released on 05 May 2024 with total hardcover pages 483. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Probabilistic Graphical Models for Genetics Genomics and Postgenomics books below.

Probabilistic Graphical Models for Genetics Genomics and Postgenomics
Probabilistic Graphical Models for Genetics Genomics and Postgenomics
Author : Christine Sinoquet
File Size : 40,7 Mb
Publisher : Oxford University Press, USA
Language : English
Release Date : 05 May 2024
ISBN : 9780198709022
Pages : 483 pages
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Probabilistic Graphical Models for Genetics Genomics and Postgenomics by Christine Sinoquet Book PDF Summary

At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data of various types, the so-called 'omics', is in need of accurate and efficient methods for modelling and PGMs are expected to have a prominent role to play. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest.

Probabilistic Graphical Models for Genetics Genomics and Postgenomics

Probabilistic Graphical Models for Genetics Genomics and Postgenomics by Christine Sinoquet,Raphaël Mourad

At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data of various types, the so-called 'omics', is in need of accurate and efficient methods for modelling and PGMs are expected to have a

Get Book
Probabilistic Graphical Models for Genetics Genomics and Postgenomics

Probabilistic Graphical Models for Genetics Genomics and Postgenomics by Christine Sinoquet,Raphael Mourad

At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data of various types, the so-called 'omics', is in need of accurate and efficient methods for modelling and PGMs are expected to have a

Get Book
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