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.

(2018). Computational challenges and temporal dependence in Bayesian nonparametric models [journal article - articolo]. In STATISTICAL METHODS & APPLICATIONS. Retrieved from http://hdl.handle.net/10446/193469

Computational challenges and temporal dependence in Bayesian nonparametric models

Argiento, Raffaele;
2018

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.
articolo
Argiento, Raffaele; Ruggiero, Matteo
(2018). Computational challenges and temporal dependence in Bayesian nonparametric models [journal article - articolo]. In STATISTICAL METHODS & APPLICATIONS. Retrieved from http://hdl.handle.net/10446/193469
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/193469
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