Buch, Englisch, 202 Seiten, Format (B × H): 154 mm x 236 mm, Gewicht: 355 g
Reihe: Surveys and Tutorials in the Applied Mathematical Sciences
Ten Lectures on Subjective Computing
Buch, Englisch, 202 Seiten, Format (B × H): 154 mm x 236 mm, Gewicht: 355 g
Reihe: Surveys and Tutorials in the Applied Mathematical Sciences
ISBN: 978-0-387-73393-7
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
- Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik
Weitere Infos & Material
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.
