Probabilistic graphical models of individual socially significant behavior on the base of incomplete data

We consider a problem of socially significant behavior rate estimates in terms of probabilistic graphical models. Such formal description of the problem allows applying powerful methods and developed algorithms of the theory of Bayesian belief networks. We can use existing software to make computational simulations and apply the model to solve practical tasks. We describe a simple model based on the incomplete data about time intervals between behavior episodes and propose ways of its development.