Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same ?eld, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference...

This is the first textbook on pattern recognition to present the Bayesian viewpoint. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible, and it uses graphical models to describe probability distributions.

The third in a series of annual anthologies produced by the American Examples workshop. In the latest volume from this innovative academic project, ten topically and methodologically diverse scholars vividly reimagine the meaning and applications of American religious history.