In this paper we study the the semantics of non-monotonic negation in probabilistic 
deductive databases.  Based on the stable semantics for classical logic programming, 
we examine three natural notions of stability: stable formula functions, stable families 
of probabilistic interpretations and stable probabilistic models. We show that stable 
formula functions are minimal fixpoints of operators associated with probabilistic logic 
programs.  We also prove that each member in a stable family of probabilistic 
interpretations is a probabilistic model of the program. Then we show that stable formula 
functions and stable families behave as duals of each other, tying together elegantly the 
fixpoint and model theories for probabilistic logic programs with negation. Furthermore, 
since a probabilistic logic program may not necessarily have a stable family of 
probabilistic interpretations, we provide a stable class semantics for such programs. 
Finally, we investigate the notion of stable probabilistic model.  We show that this 
notion, though natural, is too weak in the probabilistic framework.