Principles and Methods for Data Science (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 496
• Erscheinungstermin: 28. Mai 2020
• Englisch
• ISBN-13: 9780444642127
• Artikelnr.: 61681514

Autorenporträt

Arni S.R. Srinivasa Rao works in pure mathematics, applied mathematics, probability, artificial

intelligence and applications in medicine. He had edited these handbooks jointly with renowned statistician Dr. C. R. Rao. He is a Professor at the Medical College of Georgia,

Augusta University, U.S.A., and the Director of the Laboratory for Theory and Mathematical

Modeling housed within the Division of Infectious Diseases, Medical College of Georgia, Augusta,

U.S.A. Previously, Dr. Rao conducted research and/or taught at the Mathematical Institute, University of Oxford (2003, 2005-07), Indian Statistical Institute (1998-2002, 2006-2012), Indian Institute of Science (2002-04), University of Guelph (2004-06). Until 2012, Dr. Rao held a permanent faculty position at the Indian Statistical Institute. He has won the Heiwa-Nakajima Award (Japan) and Fast Track Young Scientists Fellowship in Mathematical Sciences (DST, New Delhi). Dr. Rao also proved a major theorem in stationary population models, such as, Rao's Partition Theorem in

Populations, Rao-Carey Theorem in stationary populations, and developed mathematical

modeling-based policies for the spread of diseases like HIV, H5N1, COVID-19, etc. He developed

a new set of network models for understanding avian pathogen biology on grid graphs (these were

called chicken walk models), AI Models for COVID-19, and received wide coverage in the science

media. Dr. Rao is an elected Fellow of ISMMACS (Indian Society for Mathematical Modeling and

Computer Simulation), and ISPS (Indian Society for Probability and Statistics). He developed

concepts such as "Exact Deep Learning Machines?, and "Multilevel Contours within a bundle of Complex Number Planes?.

Inhaltsangabe

1. Markov chain Monte Carlo methods: Theory and practice


David A. Spade




2. An information and statistical analysis pipeline for microbial metagenomic sequencing data


Shinji Nakaoka and Keisuke Ohta




3. Machine learning algorithms, applications, and practices in data science


Kalidas Yeturu




4. Bayesian model selection for high-dimensional data


Naveen Naidu Narisetty




5. Competing risks: Aims and methods


Ronald Geskus




6. High-dimensional statistical inference: Theoretical development to data analytics


Deepak Nag 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. Human life expectancy is computed from an incomplete sets of data: Modeling and analysis


Arni S.R. Srinivasa Rao and James R. Carey




10. Support vector machines: A robust prediction method with applications in bioinformatics

Arnout Van Messem