Principles and methods for data science Volume 43 [eBook] / edited by Arni S.R. Srinivasa Rao and C.R. Rao.
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| Edition | 1st ed. |
| Publication | |
| Contents | Markov chain Monte Carlo methods: Theory and practice David A. Spade An information and statistical analysis pipeline for microbial metagenomic sequencing data Shinji Nakaoka and Keisuke Ohta Machine learning algorithms, applications, and practices in data science Kalidas Yeturu Bayesian model selection for high-dimensional data Naveen Naidu Narisetty Competing risks: Aims and methods Ronald Geskus High-dimensional statistical inference: Theoretical development to data analytics Deepak Nag Ayyala Big data challenges in genomics Hongyan Xu Analysis of microarray gene expression data using information theory and stochastic algorithm Narayan Behera Human life expectancy is computed from an incomplete sets of data: Modeling and analysis Arni S.R. Srinivasa Rao and James R. Carey Support vector machines: A robust prediction method with applications in bioinformatics Arnout Van Messem. |
| Multimedia | |
| Physical Description | 1 online resource (xvii, 478 pages) : ill., digital, PDF file(s). |
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$a 1st ed.
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$a Amsterdam, Netherlands : $b North Holland, is an imprint of Elsevier, $c ©2020.
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$a 1 online resource (xvii, 478 pages) : $b ill., digital, PDF file(s).
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$a Handbook of statistics ; $v Volume 43.
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$a Includes bibliographical references and index.
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$a Markov chain Monte Carlo methods: Theory and practice David A. Spade An information and statistical analysis pipeline for microbial metagenomic sequencing data Shinji Nakaoka and Keisuke Ohta Machine learning algorithms, applications, and practices in data science Kalidas Yeturu Bayesian model selection for high-dimensional data Naveen Naidu Narisetty Competing risks: Aims and methods Ronald Geskus High-dimensional statistical inference: Theoretical development to data analytics Deepak Nag Ayyala Big data challenges in genomics Hongyan Xu Analysis of microarray gene expression data using information theory and stochastic algorithm Narayan Behera Human life expectancy is computed from an incomplete sets of data: Modeling and analysis Arni S.R. Srinivasa Rao and James R. Carey Support vector machines: A robust prediction method with applications in bioinformatics Arnout Van Messem.
520
$a Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more.
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$a Mathematical statistics.
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$a Markov processes.
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$a Monte carlo method.
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$a Quantitative research.
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$a Data mining.
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$a Bayesian statistical decision theory.
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$a Big data
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$a Rao, Arni S.R. Srinivasa, $e editor.
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$a Rao, C.R., $e editor.
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$i Printed edition: $z 9780444642110
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$a Handbook of statistics ; $v Volume 43.
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$u https://www.sciencedirect.com/handbook/handbook-of-statistics/vol/43/suppl/C $y Click here to view eBook.
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| Subject | |
| 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. |