The Computational Cost of Blocking for Sampling Discretely Observed Diffusions
نویسندگان
چکیده
Abstract Many approaches for conducting Bayesian inference on discretely observed diffusions involve imputing diffusion bridges between observations. This can be computationally challenging in settings which the temporal horizon subsequent observations is large, due to poor scaling of algorithms simulating as observation distance increases. It common practical use a blocking scheme , path split into (user-specified) number overlapping segments and Gibbs sampler employed update turn. Substituting independent simulation one obtained using introduces an inherent trade-off: we are now shorter at cost introducing dependency iterations bridge sampler. further complicated by fact that there possible ways implement scheme, each different structure iterations. Although schemes have had considerable empirical success practice, has been no analysis this trade-off nor guidance practitioners particular specifications should used obtain efficient implementation. In article conduct demonstrate expected computational blocked path-space rejection applied Brownian scales asymptotically cubic rate with respect linear case Ornstein–Uhlenbeck process. Numerical experiments suggest applicability both results our paper provide beyond class considered.
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ژورنال
عنوان ژورنال: Methodology and Computing in Applied Probability
سال: 2022
ISSN: ['1387-5841', '1573-7713']
DOI: https://doi.org/10.1007/s11009-022-09949-y