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 gppois is a Bayesian tool for learning about a smooth function based on noisy measurements of its values. Users train a model of the true function on the noisy datapoints, then use that model to make predictions at whatever points they desire to know.

UPDATE (2012-09-10): gppois is now on CRAN, and even easier to install!

Important: I have moved development to github!  The latest version of the software can always be found there.  It also has an "Issues" page, where you can easily submit any problems you may have and track their status.  Please visit the github page for gppois to keep track of all the latest updates.

NOTE: this software is associated with a paper of ours, "A Bayesian approach for denoising one-dimensional data," published in the Journal of Applied Crystallography; we provide instructions for reproducing the results of that paper.


gppois quantifies uncertainty in continuous functions, given a collection of (possibly noisy) measurements of function values.

This uncertainty usually comes from two sources:

  1. Noise: the measured values differ randomly from the true values
    • Example: X-ray powder diffraction datapoints exhibit Poisson noise, or "counting statistics"
  2. Interpolation: we know the function in one region; what does that tell us about another?


Created December 1, 2011, Updated November 15, 2019