This book has been written for undergraduate and graduate students in various disciplines of mathematics. The authors have developed a superior teaching and learning tool that makes it easy for readers to grasp new concepts and apply them in practice.

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.

Expository accessible book, internationally known authors Includes supplementary material: sn.pub/extras

Inhalt
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.