Hao Helen Zhang – författare

The idea for this book came from the time the authors spent at the Statistics and Applied Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina starting in fall 2003. The rst author was there for a total of two years, the rst year as a Duke/SAMSI Research Fellow. The second author was there for a year as a Post-Doctoral Scholar. The third author has the great fortune to be in RTP p- manently. SAMSI was – and remains – an incredibly rich intellectual environment with a general atmosphere of free-wheeling inquiry that cuts across established elds. SAMSI encourages creativity: It is the kind of place where researchers can be found at work in the small hours of the morning – computing, interpreting computations, and developing methodology. Visiting SAMSI is a unique and wonderful experience. The people most responsible for making SAMSI the great success it is include Jim Berger, Alan Karr, and Steve Marron. We would also like to express our gratitude to Dalene Stangl and all the others from Duke, UNC-Chapel Hill, and NC State, as well as to the visitors (short and long term) who were involved in the SAMSI programs. It was a magical time we remember with ongoing appreciation.

The idea for this book came from the time the authors spent at the Statistics and Applied Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina starting in fall 2003. The rst author was there for a total of two years, the rst year as a Duke/SAMSI Research Fellow. The second author was there for a year as a Post-Doctoral Scholar. The third author has the great fortune to be in RTP p- manently. SAMSI was – and remains – an incredibly rich intellectual environment with a general atmosphere of free-wheeling inquiry that cuts across established elds. SAMSI encourages creativity: It is the kind of place where researchers can be found at work in the small hours of the morning – computing, interpreting computations, and developing methodology. Visiting SAMSI is a unique and wonderful experience. The people most responsible for making SAMSI the great success it is include Jim Berger, Alan Karr, and Steve Marron. We would also like to express our gratitude to Dalene Stangl and all the others from Duke, UNC-Chapel Hill, and NC State, as well as to the visitors (short and long term) who were involved in the SAMSI programs. It was a magical time we remember with ongoing appreciation.

An essential roadmap to the application of computational statistics in contemporary data science

In Computational Statistics in Data Science, a team of distinguished mathematicians and statisticians delivers an expert compilation of concepts, theories, techniques, and practices in computational statistics for readers who seek a single, standalone sourcebook on statistics in contemporary data science. The book contains multiple sections devoted to key, specific areas in computational statistics, offering modern and accessible presentations of up-to-date techniques.

Computational Statistics in Data Science provides complimentary access to finalized entries in the Wiley StatsRef: Statistics Reference Online compendium. Readers will also find:

A thorough introduction to computational statistics relevant and accessible to practitioners and researchers in a variety of data-intensive areas Comprehensive explorations of active topics in statistics, including big data, data stream processing, quantitative visualization, and deep learning

Perfect for researchers and scholars working in any field requiring intermediate and advanced computational statistics techniques, Computational Statistics in Data Science will also earn a place in the libraries of scholars researching and developing computational data-scientific technologies and statistical graphics.

An essential roadmap to the application of computational statistics in contemporary data science In Computational Statistics in Data Science, a team of distinguished mathematicians and statisticians delivers an expert compilation of concepts, theories, techniques, and practices in computational statistics for readers who seek a single, standalone sourcebook on statistics in contemporary data science. The book contains multiple sections devoted to key, specific areas in computational statistics, offering modern and accessible presentations of up-to-date techniques. Computational Statistics in Data Science provides complimentary access to finalized entries in the Wiley StatsRef: Statistics Reference Online compendium. Readers will also find: A thorough introduction to computational statistics relevant and accessible to practitioners and researchers in a variety of data-intensive areasComprehensive explorations of active topics in statistics, including big data, data stream processing, quantitative visualization, and deep learningPerfect for researchers and scholars working in any field requiring intermediate and advanced computational statistics techniques, Computational Statistics in Data Science will also earn a place in the libraries of scholars researching and developing computational data-scientific technologies and statistical graphics.

An essential roadmap to the application of computational statistics in contemporary data science

In Computational Statistics in Data Science, a team of distinguished mathematicians and statisticians delivers an expert compilation of concepts, theories, techniques, and practices in computational statistics for readers who seek a single, standalone sourcebook on statistics in contemporary data science. The book contains multiple sections devoted to key, specific areas in computational statistics, offering modern and accessible presentations of up-to-date techniques.

Computational Statistics in Data Science provides complimentary access to finalized entries in the Wiley StatsRef: Statistics Reference Online compendium. Readers will also find:

A thorough introduction to computational statistics relevant and accessible to practitioners and researchers in a variety of data-intensive areas Comprehensive explorations of active topics in statistics, including big data, data stream processing, quantitative visualization, and deep learning

Perfect for researchers and scholars working in any field requiring intermediate and advanced computational statistics techniques, Computational Statistics in Data Science will also earn a place in the libraries of scholars researching and developing computational data-scientific technologies and statistical graphics.