3 years ago

Inference of signals with unknown correlation structure from non-linear measurements.

Jakob Knollmüller, Torsten A. Enßlin, Theo Steininger

We present a method to reconstruct auto-correlated signals together with their auto-correlation structure from non-linear, noisy measurements for arbitrary monotonous non-linearities. In the presented formulation the algorithm provides a significant speedup compared to prior implementations, allowing for a wider range of application. The non-linearity can be used to model instrument characteristics or to enforce properties on the underlying signal, such as positivity. Uncertainties on any posterior quantities can be provided due to independent samples from an approximate posterior distribution. We demonstrate the methods applicability via three examples, using different measurement instruments, non-linearities and dimensionality for both, simulated measurements and real data.

Publisher URL: http://arxiv.org/abs/1711.02955

DOI: arXiv:1711.02955v1

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.