A Sequential Particle Filter Method for Static Models

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  • 1 January 2000
    • preprint
      • Published in RePEc
Abstract
Particle filter methods are complex inference procedures, which combine importance sampling and Monte Carlo schemes in order to explore consistently a sequence of multiple distributions of interest. We show that such methods can also offer an efficient estimation tool in 'static' set-ups, in which case &pgr;(&thgr; | y-sub-1, …, y-sub-N) (n (This abstract was borrowed from another version of this item.)
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