Context : The determination of accurate stellar parameters of giant stars is essential for our understanding of such stars in general and as exoplanet host stars in particular . Precise stellar masses are vital for determining the lower mass limit of potential substellar companions with the radial velocity method , but also for dynamical modeling of multiplanetary systems and the analysis of planetary evolution . Aims : Our goal is to determine stellar parameters , including mass , radius , age , surface gravity , effective temperature and luminosity , for the sample of giants observed by the Lick planet search . Furthermore , we want to derive the probability of these stars being on the horizontal branch ( HB ) or red giant branch ( RGB ) , respectively . Methods : We compare spectroscopic , photometric and astrometric observables to grids of stellar evolutionary models using Bayesian inference . Results : We provide tables of stellar parameters , probabilities for the current post-main sequence evolutionary stage , and probability density functions for 372 giants from the Lick planet search . We find that 81 \% of the stars in our sample are more probably on the HB . In particular , this is the case for 15 of the 16 planet host stars in the sample . We tested the reliability of our methodology by comparing our stellar parameters to literature values and find very good agreement . Furthermore , we created a small test sample of 26 giants with available asteroseismic masses and evolutionary stages and compared these to our estimates . The mean difference of the stellar masses for the 24 stars with the same evolutionary stages by both methods is only \langle \Delta M \rangle = 0.01 \pm 0.20 \mathrm { M _ { \odot } } . Conclusions : We do not find any evidence for large systematic differences between our results and estimates of stellar parameters based on other methods . In particular we find no significant systematic offset between stellar masses provided by asteroseismology to our Bayesian estimates based on evolutionary models .