A Bayesian Approach to Multiscale Inverse Problems Using Sequential Monte Carlo Method

@inproceedings{Wan2010ABA,
  title={A Bayesian Approach to Multiscale Inverse Problems Using Sequential Monte Carlo Method},
  author={Jiang Wan and Nicholas Zabaras},
  year={2010}
}
Motivation We are interested in the identification of spatially varying parameters indirectly from observation data (e.g. permeability estimation in geological/reservoir engineering applications using pressure, flow or tracer time data). Interested in a Bayesian approach: forward model highly non-linear and expensive, parameter field multiscale in nature. The scale of estimation is not a priori known – multiscale framework for Bayesian inference is needed. A multiscale Bayesian model based on… CONTINUE READING

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SHOWING 1-10 OF 35 REFERENCES

Gaussian random field models for spatial data Handbook of Markov Chain Monte Carlo

  • M Haran
  • 2010
1 Excerpt

A multiresolution , non - parametric , Bayesian framework for identification of spatiallyvarying model parameters J

  • S KoutsourelakisP
  • . Comput . Phys .
  • 2009

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