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Bayesian Inference of Autoregressive Models

Kadir, Dler (2018) Bayesian Inference of Autoregressive Models. PhD thesis, University of Sheffield.

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Abstract

The principles, models and steps of Bayesian time series analysis and forecasting have been developed extensively during the past forty years. In order to estimate parameters of an autoregressive (AR) model we develop Markov chain Monte Carlo (MCMC) schemes for inference of AR model. It is our interest to propose a new prior distribution placed directly on the AR parameters of the model. Thus, we revisit the stationarity conditions to determine a flexible prior for AR model parameters.

Item Type: Thesis (PhD)
Keywords: MCMC, Estimate parameters,Stationarity conditions, Autoregressive models ,Bayesian inference and Prior distributions
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield)
Depositing User: Mr Dler Kadir
Date Deposited: 12 Jun 2018 08:51
Last Modified: 12 Jun 2018 08:51
URI: http://etheses.whiterose.ac.uk/id/eprint/20610

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