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We present preliminary results of a pulsar population synthesis of normalpulsars from the Galactic disk using a Markov Chain Monte Carlo method tobetter understand the parameter space of the assumed model. We use the Kuipertest, similar to the Kolmogorov-Smirnov test, to compare the cumulativedistributions of chosen observables of detected radio pulsars with thosesimulated for various parameters. Our code simulates pulsars at birth usingMonte Carlo techniques and evolves them to the present assuming initialspatial, kick velocity, magnetic field, and period distributions. Pulsars arespun down to the present, given radio and gamma-ray emission characteristics,filtered through ten selected radio surveys, and a {\it Fermi} all-skythreshold map. Each chain begins with a different random seed and searches aten-dimensional parameter space for regions of high probability for a total ofone thousand different simulations before ending. The code investigates boththe "large world" as well as the "small world" of the parameter space. We applythe K-means clustering algorithm to verify if the chains reveal a single ormultiple regions of significance. The outcome of the combined set of chains isthe weighted average and deviation of each of the ten parameters describing themodel. While the model reproduces reasonably well the detected distributions ofnormal radio pulsars, it does not replicate the predicted detected $\dot P - P$distribution of {\it Fermi} pulsars. The simulations do not produce sufficientnumbers of young, high-$\dot E$ pulsars in the Galactic plane.

Population Synthesis of Normal Radio and Gamma-ray Pulsars Using Markov Chain Monte Carlo Techniques