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On a full Bayesian inference for force reconstruction problems
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DOI：10.1016/j.ymssp.2017.10.023.png)
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Abstract
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In a previous paper, the authors introduced a flexible methodology for reconstructing mechanical sources in the frequency domain from prior local information on both their nature and location over a linear and time invariant structure. The proposed approach was derived from Bayesian statistics, because of its ability in mathematically accounting for experimenter's prior knowledge. However, since only the Maximum a Posteriori estimate was computed, the posterior uncertainty about the regularized solution given the measured vibration field, the mechanical model and the regularization parameter was not assessed. To answer this legitimate question, this paper fully exploits the Bayesian framework to provide, from a Markov Chain Monte Carlo algorithm, credible intervals and other statistical measures (mean, median, mode) for all the parameters of the force reconstruction problem. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
Linear inverse problem
Force reconstruction
Bayesian inference
Generalized Gaussian priors
Gibbs sampler
Hamiltonian Monte Carlo
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