Spectro-photometric distances to stars: a general-purpose Bayesian
  approach
  We developed a code that estimates distances to stars using measured
spectroscopic and photometric quantities. We employ a Bayesian approach to
build the probability distribution function over stellar evolutionary models
given these data, delivering estimates of model parameters for each star
individually. The code was first tested on simulations, successfully recovering
input distances to mock stars with <1% bias.The method-intrinsic random
distance uncertainties for typical spectroscopic survey measurements amount to
around 10% for dwarf stars and 20\% for giants, and are most sensitive to the
quality of $\log g$ measurements. The code was validated by comparing our
distance estimates to parallax measurements from the Hipparcos mission for
nearby stars (< 300 pc), to asteroseismic distances of CoRoT red giant stars,
and to known distances of well-studied open and globular clusters. The external
comparisons confirm that our distances are subject to very small systematic
biases with respect to the fundamental Hipparcos scale (+0.4 % for dwarfs, and
+1.6% for giants). The typical random distance scatter is 18% for dwarfs, and
26% for giants. For the CoRoT-APOGEE sample, the typical random distance
scatter is ~15%, both for the nearby and farther data. Our distances are
systematically larger than the CoRoT ones by about +9%, which can mostly be
attributed to the different choice of priors. The comparison to known distances
of star clusters from SEGUE and APOGEE has led to significant systematic
differences for many cluster stars, but with opposite signs, and with
substantial scatter. Finally, we tested our distances against those previously
determined for a high-quality sample of giant stars from the RAVE survey, again
finding a small systematic trend of +5% and an rms scatter of 30%.
