论文信息 - A sequential particle filter method for static models

A sequential particle filter method for static models

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 p(t | y-sub-1, …, y-sub-N) (n

N. Chopin

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