We present a simple method for fitting parametrized mass models of the Milky Way to observational constraints .
We take a Bayesian approach which allows us to take into account input from photometric and kinematic data , and expectations from theoretical modelling .
This provides us with a best-fitting model , which is a suitable starting point for dynamical modelling .
We also determine a probability density function on the properties of the model , which demonstrates that the mass distribution of the Galaxy remains very uncertain .
For our choices of parametrization and constraints , we find disc scale lengths of 3.00 \pm 0.22 \mathrm { kpc } and 3.29 \pm 0.56 \mathrm { kpc } for the thin and thick discs respectively ; a Solar radius of 8.29 \pm 0.16 \mathrm { kpc } and a circular speed at the Sun of 239 \pm 5 \mathrm { km s } ^ { -1 } ; a total stellar mass of 6.43 \pm 0.63 \times 10 ^ { 10 } { M } _ { \odot } ; a virial mass of 1.26 \pm 0.24 \times 10 ^ { 12 } { M } _ { \odot } and a local dark matter density of 0.40 \pm 0.04 \mathrm { GeV cm } ^ { -3 } .
We find some correlations between the best-fitting parameters of our models ( for example , between the disk scale lengths and the Solar radius ) , which we discuss .
The chosen disc scale-heights are shown to have little effect on the key properties of the model .