The most popular method used in traditional power system state estimation is the Maximum Likelihood Estimation (MLE). It assumes the state of the system is a set of deterministic variables and determines the most likely state via error included interval measurements. In the distribution system, the measurements are often too sparse to fulfill the system observability. Instead of introducing pseudomeasurements, we propose a Belief Propagation (BP) based distribution system state estimator. This new approach assumes that the system state is a set of stochastic variables. With a set of prior distributions, it calculates the posterior distributions of the state variables via real-time sparse measurements from both traditional measurements and the high resolution smart metering data.