Nonparametric Bayesian inference in applications

Peter Müeller (), Fernando A. Quintana and Garritt Page
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Peter Müeller: University of Texas at Austin
Fernando A. Quintana: Pontificia Universidad Católica de Chile
Garritt Page: Brigham Young University

Statistical Methods & Applications, 2018, vol. 27, issue 2, No 1, 175-206

Abstract: Abstract Nonparametric Bayesian (BNP) inference is concerned with inference for infinite dimensional parameters, including unknown distributions, families of distributions, random mean functions and more. Better computational resources and increased use of massive automated or semi-automated data collection makes BNP models more and more common. We briefly review some of the main classes of models, with an emphasis on how they arise from applied research questions, and focus in more depth only on BNP models for spatial inference as a good example of a class of inference problems where BNP models can successfully address limitations of parametric inference.

Keywords: Nonparametric inference; Bayesian inference; Dirichlet process; Polya tree (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10260-017-0405-z

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