Traditional models of migration assume that migrants move to places of greatest economic incentive, and are more likely to move when current economic conditions push migrants from their origin. Although prospective income at a destination has been a major determining factor for migration in preexisting migration models, and distance between origin and destination is also a major consideration, we take a new approach with a model that reflects migration chaining, where migrants to a city B send information back to their origin city A, and interest other members of A to migrate to B. We isolate the social factors of place-pair synergies through components from Bayes' Law: conditional probability and posterior probability of unique origin/destination migrant volume, and a system-wide probability of unique O/D transfer. These allow us to model social space as well as physical space, rather than physical space alone. We test these variables' power for predicting future migration against four other predictive models: the traditional gravity model, transit data, airline and trip data, and linear trends. We use a case study of U.S. Migration flows in a system of major cities, given annual data from 1996-2004 to predict city-to-city flows annually for 2005-2008, and find that conditional and posterior probabilities outperform system-wide probabilities, gravity, transit and linear forecast models. These probabilities also exhibit a surprising level of steady-state stationarity, and therefore are a promising avenue for more accurately modelling future migration flows.