Computational challenges and temporal dependence in Bayesian nonparametric models

Raffaele Argiento; Matteo Ruggiero

doi:10.1007/s10260-017-0397-8

Computational challenges and temporal dependence in Bayesian nonparametric models

Raffaele Argiento
, Matteo Ruggiero^*

^*Corresponding author

Faculty of Economics

University of Turin

Research output: Contribution to journal › Article › peer-review

Abstract

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

Original language	English
Pages (from-to)	231-238
Number of pages	8
Journal	Statistical Methods and Applications
Volume	27
Issue number	2
DOIs	https://doi.org/10.1007/s10260-017-0397-8
Publication status	Published - 2018

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

Keywords

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

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10.1007/s10260-017-0397-8

Cite this

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title = "Computational challenges and temporal dependence in Bayesian nonparametric models",

abstract = "Abstract M{\"u}ller et al. (Stat Methods Appl, 2017) provide an excellent review of\textbackslash{}r\textbackslash{}nseveral classes of Bayesian nonparametric models which have found widespread application in a variety of contexts, successfully highlighting their flexibility in comparison\textbackslash{}r\textbackslash{}nwith parametric families. Particular attention in the paper is dedicated to modelling\textbackslash{}r\textbackslash{}nspatial dependence. Here we contribute by concisely discussing general computational\textbackslash{}r\textbackslash{}nchallenges which arise with posterior inference with Bayesian nonparametric models\textbackslash{}r\textbackslash{}nand certain aspects of modelling temporal dependence.",

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author = "Raffaele Argiento and Matteo Ruggiero",

year = "2018",

doi = "10.1007/s10260-017-0397-8",

language = "English",

volume = "27",

pages = "231--238",

journal = "Statistical Methods and Applications",

issn = "1618-2510",

publisher = "Springer Nature",

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TY - JOUR

T1 - Computational challenges and temporal dependence in Bayesian nonparametric models

AU - Argiento, Raffaele

AU - Ruggiero, Matteo

PY - 2018

Y1 - 2018

N2 - Abstract Müller et al. (Stat Methods Appl, 2017) provide an excellent review of\r\nseveral classes of Bayesian nonparametric models which have found widespread application in a variety of contexts, successfully highlighting their flexibility in comparison\r\nwith parametric families. Particular attention in the paper is dedicated to modelling\r\nspatial dependence. Here we contribute by concisely discussing general computational\r\nchallenges which arise with posterior inference with Bayesian nonparametric models\r\nand certain aspects of modelling temporal dependence.

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

KW - Bayesian dependent model

KW - Computation

KW - Conjugacy

KW - Dirichlet

KW - Probability and Uncertainty

KW - Statistics

KW - Statistics and Probability

KW - Transition function

KW - Bayesian dependent model

KW - Computation

KW - Conjugacy

KW - Dirichlet

KW - Probability and Uncertainty

KW - Statistics

KW - Statistics and Probability

KW - Transition function

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DO - 10.1007/s10260-017-0397-8

M3 - Article

SN - 1618-2510

VL - 27

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EP - 238

JO - Statistical Methods and Applications

JF - Statistical Methods and Applications

IS - 2

ER -

Computational challenges and temporal dependence in Bayesian nonparametric models

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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