Multivariate Statistical Machine Learning Methods for Genomic Prediction is popular PDF and ePub book, written by Osval Antonio Montesinos López in 2022-02-14, it is a fantastic choice for those who relish reading online the Technology & Engineering genre. Let's immerse ourselves in this engaging Technology & Engineering book by exploring the summary and details provided below. Remember, Multivariate Statistical Machine Learning Methods for Genomic Prediction can be Read Online from any device for your convenience.
Multivariate Statistical Machine Learning Methods for Genomic Prediction Book PDF Summary
This book is open access under a CC BY 4.0 license This 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.
Detail Book of Multivariate Statistical Machine Learning Methods for Genomic Prediction PDF
- Author : Osval Antonio Montesinos López
- Release : 14 February 2022
- Publisher : Springer Nature
- ISBN : 9783030890100
- Genre : Technology & Engineering
- Total Page : 707 pages
- Language : English
- PDF File Size : 7,5 Mb
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