  We present a method to estimate distances to stars with spectroscopically
derived stellar parameters. The technique is a Bayesian approach with
likelihood estimated via comparison of measured parameters to a grid of stellar
isochrones, and returns a posterior probability density function for each
star's absolute magnitude. This technique is tailored specifically to data from
the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) survey.
Because LAMOST obtains roughly 3000 stellar spectra simultaneously within each
~5-degree diameter "plate" that is observed, we can use the stellar parameters
of the observed stars to account for the stellar luminosity function and target
selection effects. This removes biasing assumptions about the underlying
populations, both due to predictions of the luminosity function from stellar
evolution modeling, and from Galactic models of stellar populations along each
line of sight. Using calibration data of stars with known distances and stellar
parameters, we show that our method recovers distances for most stars within
~20%, but with some systematic overestimation of distances to halo giants. We
apply our code to the LAMOST database, and show that the current precision of
LAMOST stellar parameters permits measurements of distances with ~40% error
bars. This precision should improve as the LAMOST data pipelines continue to be
refined.
