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    Implicit Sampling for Path Integral Control, Monte Carlo Localization, and SLAM 

    Source: Journal of Dynamic Systems, Measurement, and Control:;2015:;volume( 137 ):;issue: 005:;page 51016
    Author(s): Morzfeld, Matthias
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Implicit sampling is a recently developed variationally enhanced sampling method that guides its samples to regions of high probability, so that each sample carries information. Implicit sampling may thus improve the ...
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    How Sampling Errors in Covariance Estimates Cause Bias in the Kalman Gain and Impact Ensemble Data Assimilation 

    Source: Monthly Weather Review:;2023:;volume( 151 ):;issue: 009:;page 2413
    Author(s): Hodyss, Daniel; Morzfeld, Matthias
    Publisher: American Meteorological Society
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    A Theory for Why Even Simple Covariance Localization Is So Useful in Ensemble Data Assimilation 

    Source: Monthly Weather Review:;2023:;volume( 151 ):;issue: 003:;page 717
    Author(s): Morzfeld, Matthias; Hodyss, Daniel
    Publisher: American Meteorological Society
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    Implicit Particle Methods and Their Connection with Variational Data Assimilation 

    Source: Monthly Weather Review:;2012:;volume( 141 ):;issue: 006:;page 1786
    Author(s): Atkins, Ethan; Morzfeld, Matthias; Chorin, Alexandre J.
    Publisher: American Meteorological Society
    Abstract: he implicit particle filter is a sequential Monte Carlo method for data assimilation that guides the particles to the high-probability regions via a sequence of steps that includes minimizations. A new and more general ...
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    A Theory for Why Even Simple Covariance Localization Is So Useful in Ensemble Data Assimilation 

    Source: Monthly Weather Review:;2023:;volume( 151 ):;issue: 003
    Author(s): Morzfeld, Matthias; Hodyss, Daniel
    Publisher: American Meteorological Society
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    DSpace software copyright © 2002-2015  DuraSpace
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