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Principles and Methods for Data Science de Arni S.R. Srinivasa Rao

Principles and Methods for Data Science: Handbook of Statistics, cartea 43

Arni S.R. Srinivasa Rao, C. R. Rao
en Limba Engleză Hardback – 26 mai 2020
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.


  • Provides the authority and expertise of leading contributors from an international board of authors
  • Presents the latest release in the Handbook of Statistics series
  • Updated release includes the latest information on Principles and Methods for Data Science
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Specificații

ISBN-13: 9780444642110
ISBN-10: 0444642110
Pagini: 496
Dimensiuni: 152 x 229 mm
Greutate: 0.83 kg
Editura: ELSEVIER SCIENCE
Seria Handbook of Statistics

Public țintă

Graduate students to senior researchers in statistics and applied mathematicians who wish to refer to very rich and authentic collection in population models and their analytical solutions to their real-world applications. Research scientists and quantitative biologists would find it fascinatingly replicative information stored in this volume.

Cuprins

    1. Markov chain Monte Carlo methods: Theory and practiceDavid A. Spade
    2. An information and statistical analysis pipeline for microbial metagenomic sequencing dataShinji Nakaoka and Keisuke Ohta
    3. Machine learning algorithms, applications, and practices in data scienceKalidas Yeturu
    4. Bayesian model selection for high-dimensional dataNaveen Naidu Narisetty
    5. Competing risks: Aims and methodsRonald Geskus
    6. High-dimensional statistical inference: Theoretical development to data analyticsDeepak Nag Ayyala
    7. Big data challenges in genomicsHongyan Xu
    8. Analysis of microarray gene expression data using information theory and stochastic algorithmNarayan Behera
    9. Human life expectancy is computed from an incomplete sets of data: Modeling and analysisArni S.R. Srinivasa Rao and James R. Carey
    10. Support vector machines: A robust prediction method with applications in bioinformatics
Arnout Van Messem