Adaptive Importance Sampling: The past, the present, and the future

@article{Bugallo2017AdaptiveIS,
  title={Adaptive Importance Sampling: The past, the present, and the future},
  author={M{\'o}nica F. Bugallo and Victor Elvira and Luca Martino and David Luengo and Joaqu{\'i}n M{\'i}guez and Petar M. Djuric},
  journal={IEEE Signal Processing Magazine},
  year={2017},
  volume={34},
  pages={60-79}
}
A fundamental problem in signal processing is the estimation of unknown parameters or functions from noisy observations. Important examples include localization of objects in wireless sensor networks [1] and the Internet of Things [2]; multiple source reconstruction from electroencephalograms [3]; estimation of power spectral density for speech enhancement [4]; or inference in genomic signal processing [5]. Within the Bayesian signal processing framework, these problems are addressed by… CONTINUE READING
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