• Computer Science
  • Published 2014

An Adaptive Population Importance Sampler: Learning from the Uncertanity

@inproceedings{Martino2014AnAP,
  title={An Adaptive Population Importance Sampler: Learning from the Uncertanity},
  author={Luca Martino and Victor Elvira and David Luengo and Jukka Corander},
  year={2014}
}
Monte Carlo (MC) methods are well-known computational techniques, widely used in different fields such as signal processing, communications and machine learning. An important class of MC methods is composed of importance sampling (IS) and its adaptive extensions, such as population Monte Carlo (PMC) and adaptive multiple IS (AMIS). In this work, we introduce a novel adaptive and iterated importance sampler using a population of proposal densities. The proposed algorithm, named adaptive… CONTINUE READING

Citations

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