Our understanding of the ecological and evolutionary conditions that permit the establishment and persistence of different bacterial species in host-associated microbial communities is incomplete. Recent work done to characterize human vaginal bacterial communities by experimental and analytical approaches has shown that idiosyncratic changes in species composition and wide fluctuations in the relative abundances of the different species are undeniably associated with specific environmental drivers. An understanding of the mechanisms, ecological processes and evolutionary routes behind the genesis of such associations and fluctuations remain an important knowledge gap. If the structure and composition of a given ecological community often alternates between distinct, widely different states, then better predictions about the chances of a dramatic community shift can be achieved using mechanistic, stochastic population dynamics models. In human bacterial communities research, there is a strong need to confront problems of risk assessment and prediction using such modeling approach. In this work, we develop a modeling framework based on the multivariate Ornstein-Uhlenbeck stochastic process to predict the unfolding of complex microbial community dynamics. We derive a suite of stochastic models derived from first biological principles will be used to evaluate time-series data on the relative abundances of bacterial species in vaginal communities. The statistical inferences done with these models have broad implications to the understanding of the processes governing the composition, structure and function of bacterial communities associated to humans. Finally, our research opens the door to a better assessment of the risk to diseases associated with responses to disturbances of human-associated microbial ecosystems.