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RT Book, Whole SR Electronic DC OPAC T1 Introduction to Bayesian Scientific Computing : Ten Lectures on Subjective Computing / by Daniela Calvetti, Erkki Somersalo T2 Surveys and Tutorials in the Applied Mathematical Sciences A1 Calvetti, Daniela A1 Somersalo, Erkki A1 SpringerLink (Online service) YR 2007 FD 2007 SP XIV, 202 p K1 Computer science K1 Computers K1 Computer mathematics K1 Probabilities K1 Statistics K1 Computer Science K1 Theory of Computation K1 Computational Science and Engineering K1 Statistics and Computing/Statistics Programs K1 Computational Mathematics and Numerical Analysis K1 Probability Theory and Stochastic Processes PB Springer New York PP New York, NY SN 9780387733944 LA English (英語) CL DC23:004.0151 NO 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. Inverse problems act as the bridge between these two fields where the goal is to estimate an unknown parameter that is not directly observable by using measured data and a mathematical model linking the observed and the unknown. Inverse problems are closely related to statistical inference problems, where the observations are used to infer on an underlying probability distribution. This connection between statistical inference and inverse problems is a central topic of the book. Inverse problems are typically ill-posed: small uncertainties in data may propagate in huge uncertainties in the estimates of the unknowns. To cope with such problems, efficient regularization techniques are developed in the framework of numerical analysis. The counterpart of regularization in the framework of statistical inference is the use prior information. This observation opens the door to a fruitful interplay between statistics and numerical analysis: the statistical framework provides a rich source of methods that can be used to improve the quality of solutions in numerical analysis, and vice versa, the efficient numerical methods bring computational efficiency to the statistical inference problems. This book is intended as an easily accessible reader for those who need numerical and statistical methods in applied sciences. NO 書誌ID=1003001012; LK [E Book]http://dx.doi.org/10.1007/978-0-387-73394-4 OL 30