Multilevel dimension-independent likelihood-informed MCMC for large-scale inverse problems
We present a non-trivial integration of dimension-independent likelihood-informed (DILI) MCMC (Cui et al 2016) and the multilevel MCMC (Dodwell et al 2015) to explore the hierarchy of posterior distributions. This integration offers several advantages: First, DILI-MCMC employs an intrinsic likelihoo...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
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5 February 2024
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| In: |
Inverse problems
Year: 2024, Jahrgang: 40, Heft: 3, Pages: 1-33 |
| ISSN: | 1361-6420 |
| DOI: | 10.1088/1361-6420/ad1e2c |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1088/1361-6420/ad1e2c Verlag, kostenfrei, Volltext: https://dx.doi.org/10.1088/1361-6420/ad1e2c 
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| Verfasserangaben: | Tiangang Cui, Gianluca Detommaso and Robert Scheichl |
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| 520 | |a We present a non-trivial integration of dimension-independent likelihood-informed (DILI) MCMC (Cui et al 2016) and the multilevel MCMC (Dodwell et al 2015) to explore the hierarchy of posterior distributions. This integration offers several advantages: First, DILI-MCMC employs an intrinsic likelihood-informed subspace (LIS) (Cui et al 2014)—which involves a number of forward and adjoint model simulations—to design accelerated operator-weighted proposals. By exploiting the multilevel structure of the discretised parameters and discretised forward models, we design a Rayleigh-Ritz procedure to significantly reduce the computational effort in building the LIS and operating with DILI proposals. Second, the resulting DILI-MCMC can drastically improve the sampling efficiency of MCMC at each level, and hence reduce the integration error of the multilevel algorithm for fixed CPU time. Numerical results confirm the improved computational efficiency of the multilevel DILI approach. | ||
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