Introduction to Bayesian Scientific Computing : Ten Lectures on Subjective Computing
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...
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| Hoofdauteurs: | , |
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| Formaat: | Livre numérique |
| Taal: | Anglais |
| Gepubliceerd in: |
New York, NY :
Springer New York
2007.
Cham : Springer Nature |
| Reeks: | Surveys and Tutorials in the Applied Mathematical Sciences
2 |
| Onderwerpen: | |
| Online toegang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Opmerking: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Introduction to Bayesian scientific computing, ten lectures on subjective computing, Daniela Cavetti, Erkki Somersalo, New York, Springer Science, 2007, 1 vol. (XIV-202 p.), Surveys and tutorials in the applied mathematical sciences, 978-0-387-73393-7 • Analysis, 1, Konrad Königsberger, 1990, Berlin, Springer, 1 vol. (XI, 360 p.), 3-540-52006-6 |
| Samenvatting: | 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. . |
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| Beschrijving item: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9780387733944 |
| ISSN: | 2199-4773 |
| Toegang: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |