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Sequential Monte Carlo with Highly Informative Observations

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Advanced Monte Carlo Methods for Complex Inference Problems

Co-author: Pierre Del Moral (University of New South Wales)

We introduce a sequential Monte Carlo (SMC) method for sampling the state of continuous-time state-space models when observations are highly informative, a situation in which standard SMC methods can perform poorly. The most extreme case is where the observations are exact—-of the state itself—-and the problem is that of simulating diffusion bridges between given starting and ending states. The basic idea is to introduce a sequence of intermediate weighting and resampling steps between observation times, guiding particles towards the ending state. A few designs that have been useful in practice are given, and demonstrated on some applied problems that feature complex models.

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

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