Speculative optimizations are increasingly becoming popular for improving program performance by allowing transformations that benefit frequently traversed program paths. Such optimizations are based on dataflow facts which are mostly true, though not always safe. Probabilistic dataflow analysis frameworks infer such facts about a program, while also providing the probability with which a fact is likely to be true. We propose a new Probabilistic Dataflow Analysis Framework which uses path profiles and information about the nesting structure of loops to obtain improved probabilities of dataflow facts.