Effective Parameter Dimension via Bayesian Model Selection in the Inverse Acoustic Scattering Problem

Abel Palafox, Marcos A. Capistrán and J. Andrés Christen

Mathematical Problems in Engineering, 2014, vol. 2014, 1-12

Abstract:

We address a prototype inverse scattering problem in the interface of applied mathematics, statistics, and scientific computing. We pose the acoustic inverse scattering problem in a Bayesian inference perspective and simulate from the posterior distribution using MCMC. The PDE forward map is implemented using high performance computing methods. We implement a standard Bayesian model selection method to estimate an effective number of Fourier coefficients that may be retrieved from noisy data within a standard formulation.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:427203

DOI: 10.1155/2014/427203

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