  Motivation: Several different threads of research have been proposed for
modeling and mining temporal data. On the one hand, approaches such as dynamic
Bayesian networks (DBNs) provide a formal probabilistic basis to model
relationships between time-indexed random variables but these models are
intractable to learn in the general case. On the other, algorithms such as
frequent episode mining are scalable to large datasets but do not exhibit the
rigorous probabilistic interpretations that are the mainstay of the graphical
models literature.
  Results: We present a unification of these two seemingly diverse threads of
research, by demonstrating how dynamic (discrete) Bayesian networks can be
inferred from the results of frequent episode mining. This helps bridge the
modeling emphasis of the former with the counting emphasis of the latter.
First, we show how, under reasonable assumptions on data characteristics and on
influences of random variables, the optimal DBN structure can be computed using
a greedy, local, algorithm. Next, we connect the optimality of the DBN
structure with the notion of fixed-delay episodes and their counts of distinct
occurrences. Finally, to demonstrate the practical feasibility of our approach,
we focus on a specific (but broadly applicable) class of networks, called
excitatory networks, and show how the search for the optimal DBN structure can
be conducted using just information from frequent episodes. Application on
datasets gathered from mathematical models of spiking neurons as well as real
neuroscience datasets are presented.
  Availability: Algorithmic implementations, simulator codebases, and datasets
are available from our website at http://neural-code.cs.vt.edu/dbn
