Reversible jump Markov chain Monte Carlo strategies for Bayesian model selection in autoregressive processes
This paper addresses the problem of Bayesian inference in autoregressive (AR) processes in the case where the correct model order is unknown. Original hierarchical prior models that allow the stationarity of the model to be enforced are proposed. Obtaining the quantities of interest, such as paramet...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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2004
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| _version_ | 1797052689812553728 |
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| author | Vermaak, J Andrieu, C Doucet, A Godsill, S |
| author_facet | Vermaak, J Andrieu, C Doucet, A Godsill, S |
| author_sort | Vermaak, J |
| collection | OXFORD |
| description | This paper addresses the problem of Bayesian inference in autoregressive (AR) processes in the case where the correct model order is unknown. Original hierarchical prior models that allow the stationarity of the model to be enforced are proposed. Obtaining the quantities of interest, such as parameter estimates, predictions of future values of the time series, posterior model-order probabilities, etc., requires integration with respect to the full posterior distribution, an operation which is analytically intractable. Reversible jump Markov chain Monte Carlo (MCMC) algorithms are developed to perform the required integration implicitly by simulating from the posterior distribution. The methods developed are evaluated in simulation studies on a number of synthetic and real data sets. |
| first_indexed | 2024-03-06T18:34:38Z |
| format | Journal article |
| id | oxford-uuid:0ace09b3-33e4-4869-8549-0cf4e8e92c00 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:34:38Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | oxford-uuid:0ace09b3-33e4-4869-8549-0cf4e8e92c002022-03-26T09:25:57ZReversible jump Markov chain Monte Carlo strategies for Bayesian model selection in autoregressive processesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:0ace09b3-33e4-4869-8549-0cf4e8e92c00EnglishSymplectic Elements at Oxford2004Vermaak, JAndrieu, CDoucet, AGodsill, SThis paper addresses the problem of Bayesian inference in autoregressive (AR) processes in the case where the correct model order is unknown. Original hierarchical prior models that allow the stationarity of the model to be enforced are proposed. Obtaining the quantities of interest, such as parameter estimates, predictions of future values of the time series, posterior model-order probabilities, etc., requires integration with respect to the full posterior distribution, an operation which is analytically intractable. Reversible jump Markov chain Monte Carlo (MCMC) algorithms are developed to perform the required integration implicitly by simulating from the posterior distribution. The methods developed are evaluated in simulation studies on a number of synthetic and real data sets. |
| spellingShingle | Vermaak, J Andrieu, C Doucet, A Godsill, S Reversible jump Markov chain Monte Carlo strategies for Bayesian model selection in autoregressive processes |
| title | Reversible jump Markov chain Monte Carlo strategies for Bayesian model selection in autoregressive processes |
| title_full | Reversible jump Markov chain Monte Carlo strategies for Bayesian model selection in autoregressive processes |
| title_fullStr | Reversible jump Markov chain Monte Carlo strategies for Bayesian model selection in autoregressive processes |
| title_full_unstemmed | Reversible jump Markov chain Monte Carlo strategies for Bayesian model selection in autoregressive processes |
| title_short | Reversible jump Markov chain Monte Carlo strategies for Bayesian model selection in autoregressive processes |
| title_sort | reversible jump markov chain monte carlo strategies for bayesian model selection in autoregressive processes |
| work_keys_str_mv | AT vermaakj reversiblejumpmarkovchainmontecarlostrategiesforbayesianmodelselectioninautoregressiveprocesses AT andrieuc reversiblejumpmarkovchainmontecarlostrategiesforbayesianmodelselectioninautoregressiveprocesses AT douceta reversiblejumpmarkovchainmontecarlostrategiesforbayesianmodelselectioninautoregressiveprocesses AT godsills reversiblejumpmarkovchainmontecarlostrategiesforbayesianmodelselectioninautoregressiveprocesses |