Generating Entities as a Markov-Modulated Poisson Process   This
example shows how to generate entities using a Markov-modulated
Poisson process, which is a Poisson process whose rate depends
on the state of a Markov chain. In particular, the process is an
interrupted Poisson process because the "off" state prevents
entity generation.  The model includes three independent on-off
modulated Markov sources so you can see how their behavior
depends on the rate of the Poisson process when the Markov chain
is in the "on" state. The Path Combiner block aggregates the
outputs of all the On-Off Modulated Markov Source subsystems.
Each of the On-Off Modulated Markov Source subsystems behaves as
follows:    • The Time-Based Entity Generator block models the
Markov chain by generating an entity each time the chain changes
state.    • The Entity Departure Event to Function-Call Event
conveys the state change to the Create Generator Selection
Variable subsystem, whose output changes from 0 to 1 or vice
versa.    • The blo