(PDF) Multivariate Statistical Machine Learning Methods for Genomic Prediction 1st ed. 2022 Edition Android

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2
Description
This book is open access under a CC BY 4.0
licenseThis open access book brings together the
latest genome base prediction models currently
being used by statisticians, breeders and data
scientists. It provides an accessible way
to understand the theory behind each statistical
learning tool, the required pre- processing, the
basics of model building, how to train
statistical learning methods, the basic R scripts
needed to implement each statistical
learning tool, and the output of each tool. To do
so, for each tool the book provides background
theory, some elements of the R statistical
software for its implementation, the conceptual
underpinnings, and at least two
illustrative examples with data from real-world
genomic selection experiments. Lastly, worked-out
examples help readers check their own
comprehension.The book will greatly appeal to
readers in plant (and animal) breeding,
geneticists and statisticians, as it provides in
a very accessible way the necessary theory,
the appropriate R code, and illustrative examples
for a complete understanding of each statistical
learning tool. In addition, it weighs the
advantages and disadvantages of each tool.
3
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Methods for Genomic Prediction 1st ed. 2022
Edition Android
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Multivariate Statistical Machine Learning Methods
for Genomic Prediction 1st ed. 2022 Edition
Android READ MAGAZINE
5
Description
This book is open access under a CC BY 4.0
licenseThis open access book brings together the
latest genome base prediction models currently
being used by statisticians, breeders and data
scientists. It provides an accessible way to
understand the theory behind each statistical
learning tool, the required pre-processing, the
basics of model building, how to train
statistical learning methods, the basic R
scripts needed to implement each statistical
learning tool, and the output of each tool. To do
so, for each tool the book provides background
theory, some elements of the R statistical
software for its implementation, the conceptual
underpinnings, and at least two illustrative
examples with data from real-world
genomic selection experiments. Lastly, worked-out
examples help readers check their own
comprehension.The book will greatly appeal to
readers in plant (and animal) breeding,
geneticists and statisticians, as it provides in
a very accessible way the necessary theory, the
appropriate R code, and illustrative examples for
a complete understanding of each statistical
learning tool. In addition, it weighs
the advantages and disadvantages of each tool.
6
(PDF) Multivariate Statistical Machine Learning
Methods for Genomic Prediction 1st ed. 2022
Edition Android