A nonparametric ensemble transform method for bayesian inference
- Many applications, such as intermittent data assimilation, lead to a recursive application of Bayesian inference within a Monte Carlo context. Popular data assimilation algorithms include sequential Monte Carlo methods and ensemble Kalman filters (EnKFs). These methods differ in the way Bayesian inference is implemented. Sequential Monte Carlo methods rely on importance sampling combined with a resampling step, while EnKFs utilize a linear transformation of Monte Carlo samples based on the classic Kalman filter. While EnKFs have proven to be quite robust even for small ensemble sizes, they are not consistent since their derivation relies on a linear regression ansatz. In this paper, we propose another transform method, which does not rely on any a priori assumptions on the underlying prior and posterior distributions. The new method is based on solving an optimal transportation problem for discrete random variables.
MetadatenVerfasserangaben: | Sebastian ReichORCiDGND |
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DOI: | https://doi.org/10.1137/130907367 |
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ISSN: | 1064-8275 |
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Titel des übergeordneten Werks (Englisch): | SIAM journal on scientific computing |
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Verlag: | Society for Industrial and Applied Mathematics |
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Verlagsort: | Philadelphia |
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Publikationstyp: | Wissenschaftlicher Artikel |
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Sprache: | Englisch |
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Jahr der Erstveröffentlichung: | 2013 |
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Erscheinungsjahr: | 2013 |
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Datum der Freischaltung: | 26.03.2017 |
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Freies Schlagwort / Tag: | Bayesian inference; Monte Carlo method; linear programming; resampling; sequential data assimilation |
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Band: | 35 |
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Ausgabe: | 4 |
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Seitenanzahl: | 12 |
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Erste Seite: | A2013 |
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Letzte Seite: | A2024 |
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Organisationseinheiten: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik |
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Peer Review: | Referiert |
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