Corpus ID: 196185002

An Adaptive Population Importance Sampler: Learning from the Uncertanity

@article{Martino2014AnAP,
  title={An Adaptive Population Importance Sampler: Learning from the Uncertanity},
  author={L. Martino and V. Elvira and D. Luengo and J. Corander},
  journal={viXra},
  year={2014}
}
  • L. Martino, V. Elvira, +1 author J. Corander
  • Published 2014
  • Computer Science
  • viXra
  • 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
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    References

    SHOWING 1-10 OF 41 REFERENCES
    An adaptive population importance sampler
    • 16
    • PDF
    Adaptive Multiple Importance Sampling
    • 181
    • PDF
    Adaptive importance sampling in general mixture classes
    • 232
    • Highly Influential
    • PDF
    A R Adaptive Multiple Importance Sampling (ARAMIS)
    • 4
    A population Monte Carlo scheme with transformed weights and its application to stochastic kinetic models
    • 56
    • PDF
    Joint Model Selection and Parameter Estimation by Population Monte Carlo Simulation
    • 29
    • PDF
    Population Monte Carlo
    • 280
    • Highly Influential
    Generalized rejection sampling schemes and applications in signal processing
    • 42
    • PDF