Bayesian Non-linear Statistical Inverse Problems
Bayesian methods based on Gaussian process priors are frequently used in statistical inverse problems arising with partial differential equations (PDEs). They can be implemented by Markov chain Monte Carlo (MCMC) algorithms. The underlying statistical models are naturally high- or infinite-dimension...
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| Tác giả chính: | |
|---|---|
| Định dạng: | Livre numérique |
| Ngôn ngữ: | Anglais |
| Được phát hành: |
Berlin :
European Mathematical Society
2023.
Berlin : |
| Loạt: | Zurich Lectures in Advanced Mathematics
|
| Truy cập trực tuyến: | Accès sur la plateforme EMS Press Accès sur la plateforme ISTEX Accès Université d'Orléans Accès INSA CVL |
| Chú thích: |
European Mathematical Society (Licence nationale) European Mathematical Society (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Bayesian Non-linear Statistical Inverse Problems, 978-3-98547-053-2 |
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| 100 | 1 | |a Nickl, Richard, |d 19..- | |
| 245 | 1 | 0 | |a Bayesian Non-linear Statistical Inverse Problems |c Richard Nickl. |
| 260 | |a Berlin : |b European Mathematical Society. | ||
| 260 | |a Berlin : |b European Mathematical Society, |c 2023. | ||
| 490 | 0 | |a Zurich Lectures in Advanced Mathematics |x 2943-4971 | |
| 500 | |a European Mathematical Society (Licence nationale) | ||
| 500 | |a European Mathematical Society (Licence nationale) | ||
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/wp-content/uploads/2022/07/EMS_ebooks_LN_CollEx_contrat.pdf | ||
| 520 | |a Bayesian methods based on Gaussian process priors are frequently used in statistical inverse problems arising with partial differential equations (PDEs). They can be implemented by Markov chain Monte Carlo (MCMC) algorithms. The underlying statistical models are naturally high- or infinite-dimensional and the present book presents a rigorous mathematical analysis of the statistical performance, and algorithmic complexity, of such methods in a natural setting of non-linear random design regression. Due to the non-linearity present in many of these inverse problems, natural least squares functionals are non-convex and the Bayesian paradigm presents an attractive alternative to optimisation-based approaches. This book develops a general theory of Bayesian inference for non-linear forward maps and rigorously considers two PDE model examples arising with Darcys problem and a Schrödinger equation. The focus is initially on statistical consistency of Gaussian process methods, and then moves on to study local fluctuations and approximations of posterior distributions by Gaussian or log-concave measures whose curvature is described by PDE mapping properties of underlying information operators. Applications to the algorithmic runtime of gradient-based MCMC methods are discussed as well as computation time lower bounds for worst case performance of some algorithms. | ||
| 776 | 0 | |t Bayesian Non-linear Statistical Inverse Problems |z 978-3-98547-053-2 | |
| 856 | 4 | |u https://doi.org/10.4171/zlam/30 |z Accès sur la plateforme EMS Press | |
| 856 | 4 | |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-4834TV2F-6 |z Accès sur la plateforme ISTEX | |
| 856 | 4 | |5 452349901:819157228 |u https://ezproxy.univ-orleans.fr/login?url=https://doi.org/10.4171/zlam/30 |z Accès Université d'Orléans | |
| 856 | 4 | |5 180339901:819157554 |u https://ezproxy.univ-orleans.fr/login?url=https://doi.org/10.4171/zlam/30 |z Accès INSA CVL | |
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