This paper considers the problem of representing complex systems that evolve
stochastically over time.  <I>Dynamic Bayesian networks</I> provide a
compact representation for stochastic processes.  Unfortunately, they are
often unwieldy since they cannot explicitly model the complex organizational
structure of many real life systems: the fact that processes are typically
composed of several interacting subprocesses, each of which can, in turn, be
further decomposed.  We propose a hierarchically structured representation
language which extends both dynamic Bayesian networks and the 
<I>object-oriented Bayesian network</I> framework of [Koller & Pfeffer,
1997], and show that our language allows us to describe such systems in a
natural and modular way.  Our language supports a natural representation for
certain system characteristics that are hard to capture using more traditional
frameworks.  For example, it allows us to represent systems where some
processes evolve at a different rate than others, or systems where the
processes interact only intermittently.  We provide a simple inference 
mechanism for our representation via translation to Bayesian networks, and
suggest ways in which the inference algorithm can exploit the additional
structure encoded in our representation.
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