A Theory of Slicing for Probabilistic Control Flow Graphs

We present a theory for slicing probabilistic imperative programs-containing random assignment and "observe" statements-represented as control flow graphs whose nodes transform probability distributions. We show that such a representation allows direct adaptation of standard machinery such as data and control dependence, postdominators, relevant variables, etc. to the probabilistic setting. We separate the specification of slicing from its implementation: first we develop syntactic conditions that a slice must satisfy; next we prove that any such slice is semantically correct; finally we give an algorithm to compute the least slice. A key feature of our syntactic conditions is that they involve two disjoint slices such that the variables of one slice are probabilistically independent of the variables of the other. This leads directly to a proof of correctness of probabilistic slicing.