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Nonparametric Bayesian inference in applications

Author

Listed:
  • Peter Müeller

    (University of Texas at Austin)

  • Fernando A. Quintana

    (Pontificia Universidad Católica de Chile)

  • Garritt Page

    (Brigham Young University)

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.

Suggested Citation

  • Peter Müeller & Fernando A. Quintana & Garritt Page, 2018. "Nonparametric Bayesian inference in applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 175-206, June.
    • Handle: RePEc:spr:stmapp:v:27:y:2018:i:2:d:10.1007_s10260-017-0405-z
    DOI: 10.1007/s10260-017-0405-z
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    2. Banerjee, Sayantan & Akbani, Rehan & Baladandayuthapani, Veerabhadran, 2019. "Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 46-69.

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