Glioblastoma Multiforme (GBM) is a malignant heterogenous cancer in the brain. We propose modeling GBM with heterogeneity in cell phenotypes using a random differential equation version of the reaction-diffusion equation, where the parameters describing diffusion (D) and proliferation (ρ) are random variables. We investigate the ability to perform the inverse problem to recover the probability distributions of D and ρ solely from spatiotemporal data, for a variety of probability distribution functions. We test the ability to perform the inverse problem for noisy synthetic data. We then examine the predicted effect of treatment, specifically, chemotherapy, when assuming such a heterogeneous population and compare with predictions from a homogeneous cell population model.