Principles and methods for data science
著者
書誌事項
Principles and methods for data science
（Handbook of statistics, v. 43）
North-Holland, c2020
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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
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