期刊
BIOMETRIKA
卷 89, 期 3, 页码 539-551出版社
BIOMETRIKA TRUST
DOI: 10.1093/biomet/89.3.539
关键词
batch importance sampling; generalised linear model; importance sampling; Markov chain Monte Carlo; metropolis-hastings; mixture model; parallel processing; particle filter
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 pi(theta\y(1),...,y(N)) (n
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