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Principles and Methods for Data Science, Vol 43

  • ID: 4894892
  • Book
  • June 2020
  • 420 Pages
  • Elsevier Science and Technology
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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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1. Competing risks, aims and methods Ronald Geskus 2. Data analysis and mining of microbial community dynamics Shinji Nakaoka 3. Support Vector Machines, a robust prediction method with applications in bioinformatics Arnout Van Messem 4. Data Science
Concepts, Algorithms and Practice Kalidas Yeturu 5. Bayesian Model Selection for Data with High Dimensions Naveen Naidu Narisetty 6. High dimensional statistical inference: theoretical development to data analytics Deepak Ayyala 7. Big data challenges in genomics Hongyan Xu 8. Analysis of microarray gene expression data using information theory and stochastic algorithm Narayan Behera 9. Hybrid Models Arni S.R. Srinivasa Rao 10. Markov Chain Monte Carlo Methods: Theory and Practice David Spade
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