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- W2954094905 abstract "The Bayesian approach provides a natural way of solving engineering inverse problems including uncertainties. The objective is to describe unknown parameters of a mathematical model based on noisy measurements. Using the Bayesian approach, the vector of unknown parameters is described by its joint probability distribution, i.e. the posterior distribution. To provide samples, Markov Chain Monte Carlo methods can be used. Their disadvantage lies in the need of repeated evaluations of the mathematical model that are computationally expensive in the case of practical problems." @default.
- W2954094905 created "2019-07-12" @default.
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- W2954094905 date "2019-04-13" @default.
- W2954094905 modified "2023-09-27" @default.
- W2954094905 title "The Use of Radial Basis Function Surrogate Models for Sampling Process Acceleration in Bayesian Inversion" @default.
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- W2954094905 doi "https://doi.org/10.1007/978-3-030-14907-9_23" @default.
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