New model for non-homogeneous time process that extends dynamic Bayesian networks.

Incremental learning heuristic avoids overfitting, including the scarce data case.

The approach is neither sampling-based nor assumes smooth transitions.

Extensive simulations learnt fitter (non-)homogeneous models than state of the art.

Better fitted models and new clinical insights drawn on psychotic depression case.