Markov chain Monte Carlo estimation of autoregressive models with application to metal pollutant concentration in sludge

G. Barnett, R. Kohn, S. Sheather, J. Wong

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This paper provides an introduction to and an overview of Bayesian estimation, based on Markov chain Monte Carlo techniques. Autoregressive time series models are considered in some detail. Finally, an example involving the modelling of a metal pollutant concentration in sludge is presented.

Original languageEnglish
Pages (from-to)7-13
Number of pages7
JournalMathematical and Computer Modelling
Volume22
Issue number10-12
DOIs
StatePublished - 1995

Keywords

  • Autoregressive models
  • Gibbs sampling
  • Metropolis
  • Missing values
  • Outliers

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications

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