Daniela Calvetti, E. Somersalo

An Introduction to Bayesian Scientific Computing (eBook, PDF)

Ten Lectures on Subjective Computing

• Format: PDF

Produktbeschreibung

A combination of the concepts subjective - or Bayesian - statistics and scientific computing, the book provides an integrated view across numerical linear algebra and computational statistics. This book has been written for undergraduate and graduate students in various areas of mathematics and its applications. It is for students who are willing to get acquainted with Bayesian approach to computational science but not necessarily to go through the full immersion into the statistical analysis. It has also been written for researchers working in areas where mathematical and statistical modeling are of central importance, such as biology and engineering. This book, written by internationally known authors, is intended as an easily accessible read for those who need numerical and statistical methods in applied sciences.

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Produktdetails

• Verlag: Springer New York
• Seitenzahl: 202
• Erscheinungstermin: 20. November 2007
• Englisch
• ISBN-13: 9780387733944
• Artikelnr.: 43728463

Autorenporträt

Erkki Somersalo, Helsinki University of Technology, Helsinki, Finland / D. Calvetti, Case Western Reserve University, Cleveland, OH, USA

Inhaltsangabe

Inverse problems and subjective computing.- Basic problem of statistical inference.- The praise of ignorance: randomness as lack of information.- Basic problem in numerical linear algebra.- Sampling: first encounter.- Statistically inspired preconditioners.- Conditional Gaussian densities and predictive envelopes.- More applications of the Gaussian conditioning.- Sampling: the real thing.- Wrapping up: hypermodels, dynamic priorconditioners and Bayesian learning.

Rezensionen

From the reviews:

"This witty, erudite, and surprisingly practical book is made up of ten chapters. ... A central topic of the book is the relationship between statistical inference and the inverse problems that define Bayesian (subjective) statistics. ... This excellent book will be valuable to scientists of various stripes, statisticians, numerical analysts, those who work in image processing, and those who implement Bayesian belief nets." (George Hacken, ACM Computing Reviews, Vol. 49 (11), November, 2008)

"Introduction to Bayesian Scientific Computing is a 200-page, easily accessible, pleasant introduction fusing Bayesian approaches with numerical linear algebra methods for inverse problems ... . What I like most about this book is the apparent enthusiasm of the authors and their genuine interest in explaining rather than showing off. This enthusiasm is contagious, and the result is very readable." (Uri Ascher, The Mathematical Intelligencer, Vol. 31 (1), 2009)