Recent years have seen an increased interplay between probabilistic reasoning and formal methods. On the one hand, formal methods are used for specification, verification and development of modern complex systems and many such systems increasingly include quantitative information. Analyzing robustness and reliability of such systems often involves reasoning about uncertainty and imprecision in these systems. A wide variety of different probabilistic models and techniques have been studied under this umbrella. On the other hand, with the unprecedented adoption of machine learning systems which are based on core probabilistic methods, the usage of formal reasoning aids in boosting the end user's confidence in the actions of such systems. The goal of this workshop is to showcase some recent fundamental developments and trends at the intersection of probabilistic reasoning and formal methods and discuss the next frontier of challenges and opportunities.