  Spatio-temporal data is intrinsically high dimensional, so unsupervised
modeling is only feasible if we can exploit structure in the process. When the
dynamics are local in both space and time, this structure can be exploited by
splitting the global field into many lower-dimensional "light cones". We review
light cone decompositions for predictive state reconstruction, introducing
three simple light cone algorithms. These methods allow for tractable inference
of spatio-temporal data, such as full-frame video. The algorithms make few
assumptions on the underlying process yet have good predictive performance and
can provide distributions over spatio-temporal data, enabling sophisticated
probabilistic inference.
