Computational challenges and temporal dependence in Bayesian nonparametric models

Raffaele Argiento and Matteo Ruggiero ()
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Raffaele Argiento: University of Torino and Collegio Carlo Alberto
Matteo Ruggiero: University of Torino and Collegio Carlo Alberto

Statistical Methods & Applications, 2018, vol. 27, issue 2, No 5, 238 pages

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.

Keywords: Bayesian dependent model; Conjugacy; Computation; Dirichlet; Transition function (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10260-017-0397-8

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