  Most real-world dynamic systems are composed of different components that
often evolve at very different rates. In traditional temporal graphical models,
such as dynamic Bayesian networks, time is modeled at a fixed granularity,
generally selected based on the rate at which the fastest component evolves.
Inference must then be performed at this fastest granularity, potentially at
significant computational cost. Continuous Time Bayesian Networks (CTBNs) avoid
time-slicing in the representation by modeling the system as evolving
continuously over time. The expectation-propagation (EP) inference algorithm of
Nodelman et al. (2005) can then vary the inference granularity over time, but
the granularity is uniform across all parts of the system, and must be selected
in advance. In this paper, we provide a new EP algorithm that utilizes a
general cluster graph architecture where clusters contain distributions that
can overlap in both space (set of variables) and time. This architecture allows
different parts of the system to be modeled at very different time
granularities, according to their current rate of evolution. We also provide an
information-theoretic criterion for dynamically re-partitioning the clusters
during inference to tune the level of approximation to the current rate of
evolution. This avoids the need to hand-select the appropriate granularity, and
allows the granularity to adapt as information is transmitted across the
network. We present experiments demonstrating that this approach can result in
significant computational savings.
