An Introduction to Bayesian Scientific Computing : Ten Lectures on Subjective Computing
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The work An Introduction to Bayesian Scientific Computing : Ten Lectures on Subjective Computing represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
The Resource
An Introduction to Bayesian Scientific Computing : Ten Lectures on Subjective Computing
Resource Information
The work An Introduction to Bayesian Scientific Computing : Ten Lectures on Subjective Computing represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 An Introduction to Bayesian Scientific Computing : Ten Lectures on Subjective Computing
 Title remainder
 Ten Lectures on Subjective Computing
 Statement of responsibility
 by Daniela Calvetti, E. Somersalo
 Subject

 Computer science  Mathematics
 Distribution (Probability theory
 Information theory
 Mathematical statistics
 Probability Theory and Stochastic Processes
 Statistics and Computing/Statistics Programs
 Theory of Computation
 Computational Mathematics and Numerical Analysis
 Computational Science and Engineering
 Computer science
 Language

 eng
 eng
 Summary
 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 illposed: 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.
 Dewey number
 519.5/42
 http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut

 ZlWasX5VU3E
 PofWXM8XDHA
 Language note
 English
 LC call number
 QA75.576.95
 Literary form
 non fiction
 Nature of contents
 dictionaries
 Series statement
 Surveys and Tutorials in the Applied Mathematical Sciences,
 Series volume
 2
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