Principles and methods for data science

Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Sup...

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Bibliographic Details
Main Authors Srinivasa Rao, Arni S. R., Rao, Calyampudi Radhakrishna
Format eBook Book
LanguageEnglish
Published Amsterdam North-Holland 2020
Elsevier Science & Technology
Edition1
SeriesHandbook of Statistics
Subjects
Bayesian statistical decision theory
Markov processes
Mathematical statistics
Monte Carlo method
Quantitative research
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ISBN0444642110
9780444642110

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Summary:Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more.
Bibliography:Includes bibliographical references and index
ISBN:0444642110
9780444642110