Abstract
Estimating a deterministic single value for model parameters when reconstructing the system response has a limited meaning if one considers that the model used to predict its behaviour is just an idealization of reality, and furthermore, the existence of measurements errors. To provide a reliable answer, probabilistic instead of deterministic values should be provided, which carry information about the degree of uncertainty or plausibility of those model parameters providing one or more observations of the system response. This is widelyknown as the Bayesian inverse problem, which has been covered in the literature from different perspectives, depending on the interpretation or the meaning assigned to the probability. In this paper, we revise two main approaches: the one that uses probability as logic, and an alternative one that interprets it as information content. The contribution of this paper is to provide an unifying formulation from which both approaches stem as interpretations, and which is more general in the sense of requiring fewer axioms, at the time the formulation and computation is simplified by dropping some constants. An extension to the problem of model class selection is derived, which is particularly simple under the proposed framework. A numerical example is finally given to illustrate the utility and effectiveness of the method.
Original language  English 

Pages (fromto)  448464 
Number of pages  17 
Journal  Inverse Problems in Science and Engineering 
Volume  24 
Issue number  3 
DOIs  
Publication status  Published  10 Jun 2015 
Keywords
 Bayesian updating
 inverse problem
 model class selection
 stochastic inverse problem
 inference
 probability logic
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Extraordinary PhD Award
ChiachioRuano, J. (Recipient), Nov 2018
Prize: Prize (including medals and awards)