Principles and Methods for Data Science
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...
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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.
Bibliographische Angaben
- Herausgegeben:Srinivasa Rao, Arni S.R.; Rao, C. R.
- Verlag: North-Holland
- EAN: 9780444642110
Autoren-Porträt
Arni S.R. Srinivasa Rao works in pure mathematics, applied mathematics, probability, and artificial intelligence and applications in medicine. 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 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. Recently, he developed concepts such as "Exact Deep Learning Machines?, and "Multilevel Contours? within a bundle of Complex Number Planes.
Inhaltsverzeichnis zu „Principles and Methods for Data Science “
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 predictionmethod with applications in bioinformatics
Arnout Van Messem
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