Aircraft encounter models can be used in a variety of analyses, including collision avoidance system safety assessment, sensor design trade studies, and visual acquisition analysis. This paper presents an approach to airspace encounter model construction based on Markov models estimated from radar data. We use Bayesian networks to represent the distribution over initial states and dynamic Bayesian networks to represent transition probabilities. We apply Bayesian statistical techniques to identify the relationships between the variables in the model to best leverage a large volume of raw aircraft track data obtained from more than 130 radars across the United States.