Computational challenges and temporal dependence in Bayesian nonparametric models

Raffaele Argiento, Matteo Ruggiero

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

Abstract

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.
Lingua originaleEnglish
pagine (da-a)231-238
Numero di pagine8
RivistaSTATISTICAL METHODS & APPLICATIONS
Volume27
DOI
Stato di pubblicazionePubblicato - 2018

Keywords

  • Bayesian dependent model
  • Computation
  • Conjugacy
  • Dirichlet
  • Probability and Uncertainty
  • Statistics
  • Statistics and Probability
  • Transition function

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