Sequential Monte Carlo with highly informative observations
We propose sequential Monte Carlo (SMC) methods for sampling the posterior distribution of state-space models under highly informative observation regimes, a situation in which standard SMC methods can perform poorly. A special case is simulating bridges between given initial and final values. The b...
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| Format: | Journal article |
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Society for Industrial and Applied Mathematics
2015
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