  We discuss how to define a kernel for Signal Temporal Logic (STL) formulae.
Such a kernel allows us to embed the space of formulae into a Hilbert space,
and opens up the use of kernel-based machine learning algorithms in the context
of STL. We show an application of this idea to a regression problem in formula
space for probabilistic models.
