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The MOP - Particles without Resampling

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Advanced Monte Carlo Methods for Complex Inference Problems

Under certain assumptions, it is demonstrated that it is possible to derive a particle filter for arbitrarily high dimensions where the weights on the particles are all equal and there is therefore never any need for resampling. Relaxing these assumptions (eg by using ideas from “particle flow” implemented using piecewise linear approximations to a non-linear function) results in particle filters that only require very infrequent resampling. Results are demonstrated on non-linear non-Gaussian problems.

This talk is part of the Isaac Newton Institute Seminar Series series.

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