Zusammenfassung
We introduce Ensemble Rejection Sampling, a scheme for exact simulation from
the posterior distribution of the latent states of a class of non-linear
non-Gaussian state-space models. Ensemble Rejection Sampling relies on a
proposal for the high-dimensional state sequence built using ensembles of state
samples. Although this algorithm can be interpreted as a rejection sampling
scheme acting on an extended space, we show under regularity conditions that
the expected computational cost to obtain an exact sample increases cubically
with the length of the state sequence instead of exponentially for standard
rejection sampling. We demonstrate this methodology by sampling exactly state
sequences according to the posterior distribution of a stochastic volatility
model and a non-linear autoregressive process. We also present an application
to rare event simulation.
Nutzer