Stochastic Partial Differential Equations as priors in ensemble methods for solving inverse problems
This article describes a coordinate free approach to modelling stochastic textures through the application of stochastic partial differential equations. The intended application is that of sampling from a prior probability density in the solution of inverse problems by Bayesian filtering methods. In...
| Main Authors: | , , |
|---|---|
| Format: | Journal article |
| Published: |
2009
|
| _version_ | 1797069890648014848 |
|---|---|
| author | Potsepaev, R Farmer, C Aziz, M |
| author_facet | Potsepaev, R Farmer, C Aziz, M |
| author_sort | Potsepaev, R |
| collection | OXFORD |
| description | This article describes a coordinate free approach to modelling stochastic textures through the application of stochastic partial differential equations. The intended application is that of sampling from a prior probability density in the solution of inverse problems by Bayesian filtering methods. In simpler cases analytical formulae for the correlation functions can be derived. Such formulae can then be used to guide parameter selection in the general case where numerical methods are necessary. |
| first_indexed | 2024-03-06T22:31:07Z |
| format | Journal article |
| id | oxford-uuid:584ec16a-4556-46c4-84d8-d8be08bcac78 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T22:31:07Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | oxford-uuid:584ec16a-4556-46c4-84d8-d8be08bcac782022-03-26T17:02:28ZStochastic Partial Differential Equations as priors in ensemble methods for solving inverse problemsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:584ec16a-4556-46c4-84d8-d8be08bcac78Mathematical Institute - ePrints2009Potsepaev, RFarmer, CAziz, MThis article describes a coordinate free approach to modelling stochastic textures through the application of stochastic partial differential equations. The intended application is that of sampling from a prior probability density in the solution of inverse problems by Bayesian filtering methods. In simpler cases analytical formulae for the correlation functions can be derived. Such formulae can then be used to guide parameter selection in the general case where numerical methods are necessary. |
| spellingShingle | Potsepaev, R Farmer, C Aziz, M Stochastic Partial Differential Equations as priors in ensemble methods for solving inverse problems |
| title | Stochastic Partial Differential Equations as priors in ensemble methods for solving inverse problems |
| title_full | Stochastic Partial Differential Equations as priors in ensemble methods for solving inverse problems |
| title_fullStr | Stochastic Partial Differential Equations as priors in ensemble methods for solving inverse problems |
| title_full_unstemmed | Stochastic Partial Differential Equations as priors in ensemble methods for solving inverse problems |
| title_short | Stochastic Partial Differential Equations as priors in ensemble methods for solving inverse problems |
| title_sort | stochastic partial differential equations as priors in ensemble methods for solving inverse problems |
| work_keys_str_mv | AT potsepaevr stochasticpartialdifferentialequationsaspriorsinensemblemethodsforsolvinginverseproblems AT farmerc stochasticpartialdifferentialequationsaspriorsinensemblemethodsforsolvinginverseproblems AT azizm stochasticpartialdifferentialequationsaspriorsinensemblemethodsforsolvinginverseproblems |