  Complex cyber-physical systems interact in real-time and must consider both
timing and uncertainty. Developing software for such systems is expensive and
difficult, especially when modeling, inference, and real-time behavior must be
developed from scratch. Recently, a new kind of language has emerged -- called
probabilistic programming languages (PPLs) -- that simplify modeling and
inference by separating the concerns between probabilistic modeling and
inference algorithm implementation. However, these languages have primarily
been designed for offline problems, not online real-time systems. In this
paper, we combine PPLs and real-time programming primitives by introducing the
concept of real-time probabilistic programming languages (RTPPL). We develop an
RTPPL called ProbTime and demonstrate its usability on an automotive testbed
performing indoor positioning and braking. Moreover, we study fundamental
properties and design alternatives for runtime behavior, including a new
fairness-guided approach that automatically optimizes the accuracy of a
ProbTime system under schedulability constraints.
