Probabilistic programming is en vogue. It is used to describe
complex Bayesian networks, quantum programs, security protocols and
biological systems. Programming languages like C, C#, Java, Prolog,
Scala, etc. all have their probabilistic version. Key features are
random sampling and means to adjust distributions based on obtained
information from measurements and system observations. We show some
semantic intricacies, argue that termination is more involved than the
halting problem, and discuss recursion as well as run-time analysis.