  In many applications, observed data are influenced by some combination of
latent causes. For example, suppose sensors are placed inside a building to
record responses such as temperature, humidity, power consumption and noise
levels. These random, observed responses are typically affected by many
unobserved, latent factors (or features) within the building such as the number
of individuals, the turning on and off of electrical devices, power surges,
etc. These latent factors are usually present for a contiguous period of time
before disappearing; further, multiple factors could be present at a time. This
paper develops new probabilistic methodology and inference methods for random
object generation influenced by latent features exhibiting temporal
persistence. Every datum is associated with subsets of a potentially infinite
number of hidden, persistent features that account for temporal dynamics in an
observation. The ensuing class of dynamic models constructed by adapting the
Indian Buffet Process --- a probability measure on the space of random,
unbounded binary matrices --- finds use in a variety of applications arising in
operations, signal processing, biomedicine, marketing, image analysis, etc.
Illustrations using synthetic and real data are provided.
