  This paper deals with learning stability of partially observed switched
linear systems under arbitrary switching. Such systems are widely used to
describe cyber-physical systems which arise by combining physical systems with
digital components. In many real-world applications, the internal states cannot
be observed directly. It is thus more realistic to conduct system analysis
using the outputs of the system. Stability is one of the most frequent
requirement for safety and robustness of cyber-physical systems. Existing
methods for analyzing stability of switched linear systems often require the
knowledge of the parameters and/or all the states of the underlying system. In
this paper, we propose an algorithm for deciding stability of switched linear
systems under arbitrary switching based purely on observed output data. The
proposed algorithm essentially relies on an output-based Lyapunov stability
framework and returns an estimate of the joint spectral radius (JSR). We also
prove a probably approximately correct error bound on the quality of the
estimate of the JSR from the perspective of statistical learning theory.
