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Posterior consistency can be thought of as a theoretical justification of the Bayesian method. One of the most popular approaches to nonparametric Bayesian regression is to put a nonparametric prior distribution on the unknown regression function using Gaussian processes. In this paper, we study posterior consistency in nonparametric regression problems(More)
  • Larry Wasserman, Springer Berlin, Heidelberg New, York Barcelona, Hong Kong, London Milan +14 others
Preface There are many books on various aspects of nonparametric inference such as density estimation, nonparametric regression, bootstrapping, and wavelets methods. But it is hard to find all these topics covered in one place. The goal of this text is to provide the reader with a single book where they can find a brief account of many of the modern topics(More)