Adaptive Sequential MCMC for Combined State and Parameter Estimation
In the case of a linear state space model, we implement an MCMC sampler with two phases. In the learning phase, a self-tuning sampler is used to learn the parameter mean and covariance structure. In the estimation phase, the parameter mean and covariance structure informs the proposed mechanism a...
| Main Authors: | , , |
|---|---|
| Format: | Journal Article |
| Published: |
2018
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/78086 |
| _version_ | 1848763931841003520 |
|---|---|
| author | Cao, Zhanglong Bryant, David Parry, Matthew |
| author_facet | Cao, Zhanglong Bryant, David Parry, Matthew |
| author_sort | Cao, Zhanglong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In the case of a linear state space model, we implement an MCMC sampler with
two phases. In the learning phase, a self-tuning sampler is used to learn the
parameter mean and covariance structure. In the estimation phase, the parameter
mean and covariance structure informs the proposed mechanism and is also used
in a delayed-acceptance algorithm. Information on the resulting state of the
system is given by a Gaussian mixture. In on-line mode, the algorithm is
adaptive and uses a sliding window approach to accelerate sampling speed and to
maintain appropriate acceptance rates. We apply the algorithm to joined state
and parameter estimation in the case of irregularly sampled GPS time series
data. |
| first_indexed | 2025-11-14T11:11:18Z |
| format | Journal Article |
| id | curtin-20.500.11937-78086 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:11:18Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-780862020-09-01T05:03:02Z Adaptive Sequential MCMC for Combined State and Parameter Estimation Cao, Zhanglong Bryant, David Parry, Matthew stat.AP stat.AP In the case of a linear state space model, we implement an MCMC sampler with two phases. In the learning phase, a self-tuning sampler is used to learn the parameter mean and covariance structure. In the estimation phase, the parameter mean and covariance structure informs the proposed mechanism and is also used in a delayed-acceptance algorithm. Information on the resulting state of the system is given by a Gaussian mixture. In on-line mode, the algorithm is adaptive and uses a sliding window approach to accelerate sampling speed and to maintain appropriate acceptance rates. We apply the algorithm to joined state and parameter estimation in the case of irregularly sampled GPS time series data. 2018 Journal Article http://hdl.handle.net/20.500.11937/78086 restricted |
| spellingShingle | stat.AP stat.AP Cao, Zhanglong Bryant, David Parry, Matthew Adaptive Sequential MCMC for Combined State and Parameter Estimation |
| title | Adaptive Sequential MCMC for Combined State and Parameter Estimation |
| title_full | Adaptive Sequential MCMC for Combined State and Parameter Estimation |
| title_fullStr | Adaptive Sequential MCMC for Combined State and Parameter Estimation |
| title_full_unstemmed | Adaptive Sequential MCMC for Combined State and Parameter Estimation |
| title_short | Adaptive Sequential MCMC for Combined State and Parameter Estimation |
| title_sort | adaptive sequential mcmc for combined state and parameter estimation |
| topic | stat.AP stat.AP |
| url | http://hdl.handle.net/20.500.11937/78086 |