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Computational challenges and temporal dependence in Bayesian nonparametric models

Author

Listed:
  • Raffaele Argiento

    (University of Torino and Collegio Carlo Alberto)

  • Matteo Ruggiero

    (University of Torino and Collegio Carlo Alberto)

Abstract

Müller et al. (Stat Methods Appl, 2017) provide an excellent review of several classes of Bayesian nonparametric models which have found widespread application in a variety of contexts, successfully highlighting their flexibility in comparison with parametric families. Particular attention in the paper is dedicated to modelling spatial dependence. Here we contribute by concisely discussing general computational challenges which arise with posterior inference with Bayesian nonparametric models and certain aspects of modelling temporal dependence.

Suggested Citation

  • Raffaele Argiento & Matteo Ruggiero, 2018. "Computational challenges and temporal dependence in Bayesian nonparametric models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 231-238, June.
    • Handle: RePEc:spr:stmapp:v:27:y:2018:i:2:d:10.1007_s10260-017-0397-8
    DOI: 10.1007/s10260-017-0397-8
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    References listed on IDEAS

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    2. C. Yau & O. Papaspiliopoulos & G. O. Roberts & C. Holmes, 2011. "Bayesian non‐parametric hidden Markov models with applications in genomics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 37-57, January.
    3. Pitt, Michael K. & Walker, Stephen G., 2005. "Constructing Stationary Time Series Models Using Auxiliary Variables With Applications," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 554-564, June.
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