Numerical computation of convolutions in free probability theory

Mathematics – Probability

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

We develop a numerical approach for computing the additive, multiplicative and compressive convolution operations from free probability theory. We utilize the regularity properties of free convolution to identify (pairs of) `admissible' measures whose convolution results in a so-called `invertible measure' which is either a smoothly-decaying measure supported on the entire real line (such as the Gaussian) or square-root decaying measure supported on a compact interval (such as the semi-circle). This class of measures is important because these measures along with their Cauchy transforms can be accurately represented via a Fourier or Chebyshev series expansion, respectively. Thus knowledge of the functional inverse of their Cauchy transform suffices for numerically recovering the invertible measure via a non-standard yet well-behaved Vandermonde system of equations. We describe explicit algorithms for computing the inverse Cauchy transform alluded to and recovering the associated measure with spectral accuracy.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Numerical computation of convolutions in free probability theory does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Numerical computation of convolutions in free probability theory, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Numerical computation of convolutions in free probability theory will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-301260

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.