  Finding patterns in data is one of the most challenging open questions in
information science. The number of possible relationships scales
combinatorially with the size of the dataset, overwhelming the exponential
increase in availability of computational resources. Physical insights have
been instrumental in developing efficient computational heuristics. Using
quantum field theory methods and rethinking three centuries of Bayesian
inference, we formulated the problem in terms of finding landscapes of patterns
and solved this problem exactly. The generality of our calculus is illustrated
by applying it to handwritten digit images and to finding structural features
in proteins from sequence alignments without any presumptions about model
priors suited to specific datasets. Landscapes of patterns can be uncovered on
a desktop computer in minutes.
