Pattern theory is a distinctive approach to the analysis of all
forms of real-world signals. At its core is the design of a large
variety of probabilistic models whose samples reproduce the look
and feel of the real signals, their patterns, and their
variability. Bayesian statistical inference then allows you to
apply these models in the analysis of new signals.
This book treats the mathematical tools, the models themselves,
and the computational algorithms for applying statistics to analyze
six representative classes of signals of increasing complexity. The
book covers patterns in text, sound, and images. Discussions of
images include recognizing characters, textures, nature scenes, and
human faces. The text includes online access to the materials
(data, code, etc.) needed for the exercises.
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