  We present a new method to distinguish between different states (e.g., high
and low, quiescent and flaring) in astronomical sources with count data. The
method models the underlying physical process as latent variables following a
continuous-space Markov chain that determines the expected Poisson counts in
observed light curves in multiple passbands. For the underlying state process,
we consider several autoregressive processes, yielding continuous-space hidden
Markov models of varying complexity. Under these models, we can infer the state
that the object is in at any given time. The state predictions from these
models are then dichotomized with the help of a finite-mixture model to produce
state classifications. We apply these techniques to X-ray data from the active
dMe flare star EV Lac, splitting the data into quiescent and flaring states. We
find that a first-order vector autoregressive process efficiently separates
flaring from quiescence: flaring occurs over 30-40% of the observation
durations, a well-defined persistent quiescent state can be identified, and the
flaring state is characterized by higher temperatures and emission measures.
