  We present a novel approach to learn the formulae characterising the emergent
behaviour of a dynamical system from system observations. At a high level, the
approach starts by devising a statistical dynamical model of the system which
optimally fits the observations. We then propose general optimisation
strategies for selecting high support formulae (under the learnt model of the
system) either within a discrete set of formulae of bounded complexity, or a
parametric family of formulae. We illustrate and apply the methodology on an
in-depth case study of characterising cardiac malfunction from
electro-cardiogram data, where our approach enables us to quantitatively
determine the diagnostic power of a formula in discriminating between different
cardiac conditions.
