Noise is a major problem in analyzing tracking data of cargos moved by molecular motors. We use Bayesian
statistics to incorporate what is known about the noise in parsing the trajectory of a cargo into a series of constant velocity
segments. Tracks with just noise and no underlying motion are fit with constant velocity segments to produce a calibration curve
of fit quality versus average segment duration. Fits to tracks of moving cargos are compared to the calibration curves with
similar noise. The fit with the optimum number of constant velocity states has the least number of segments needed to match
the fit quality of the calibration curve. We have tested this approach using tracks with known underlying motion generated by
computer simulations and with a specially designed in vitro experiment. We present the results of using this parsing approach to
analyze transport of lipid droplets in Drosophila embryos.