An Adaptive Population Importance Sampler: Learning From Uncertainty
نویسندگان
چکیده
منابع مشابه
An Adaptive Population Importance Sampler: Learning from Errors
Monte Carlo (MC) methods are well-known computational techniques in different fields as signal processing, communications, and machine learning. An important class of MC methods is composed of importance sampling (IS) and its adaptive extensions, e.g., Adaptive Multiple IS (AMIS) and Population Monte Carlo (PMC). In this work, we introduce an adaptive and iterated importance sampler using a pop...
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2015
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2015.2440215