  Many astrophysical analyses depend on estimates of redshifts (a proxy for
distance) determined from photometric (i.e., imaging) data alone. Inaccurate
estimates of photometric redshift uncertainties can result in large systematic
errors. However, probability distribution outputs from many photometric
redshift methods do not follow the frequentist definition of a Probability
Density Function (PDF) for redshift -- i.e., the fraction of times the true
redshift falls between two limits $z_{1}$ and $z_{2}$ should be equal to the
integral of the PDF between these limits. Previous works have used the global
distribution of Probability Integral Transform (PIT) values to re-calibrate
PDFs, but offsetting inaccuracies in different regions of feature space can
conspire to limit the efficacy of the method. We leverage a recently developed
regression technique that characterizes the local PIT distribution at any
location in feature space to perform a local re-calibration of photometric
redshift PDFs. Though we focus on an example from astrophysics, our method can
produce PDFs which are calibrated at all locations in feature space for any use
case.
