In this paper we discuss how a Gaussian random field with Matérn covariance function can represent our prior uncertainty about the log-spectral density, $g(\omega)$, of a Gaussian, short memory time series. Hyperparameters can be suitably tuned in order to determine the mean square differentiability and the range of autocorrelation of the random field $g(\omega)$. However, Bayesian computations cannot be easily performed under such prior elicitations. We suggest therefore to approximate the Gaussian random field priors with a class of Gaussian Markov random fields which preserve the main features of the genuine prior distributions.

Random field priors for spectral density functions

TONELLATO, Stefano Federico
2007-01-01

Abstract

In this paper we discuss how a Gaussian random field with Matérn covariance function can represent our prior uncertainty about the log-spectral density, $g(\omega)$, of a Gaussian, short memory time series. Hyperparameters can be suitably tuned in order to determine the mean square differentiability and the range of autocorrelation of the random field $g(\omega)$. However, Bayesian computations cannot be easily performed under such prior elicitations. We suggest therefore to approximate the Gaussian random field priors with a class of Gaussian Markov random fields which preserve the main features of the genuine prior distributions.
File in questo prodotto:
File Dimensione Formato  
stat_plann.pdf

non disponibili

Tipologia: Documento in Post-print
Licenza: Accesso chiuso-personale
Dimensione 468.88 kB
Formato Adobe PDF
468.88 kB Adobe PDF   Visualizza/Apri

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/30272
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 3
social impact