Bayesian Adaptive Design for State-space Models with Covariates
30 mins 51 secs,
428.15 MB,
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About this item
| Description: |
Sahu, S (Southampton)
Thursday 01 September 2011, 11:00-11:30 |
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| Created: | 2011-09-05 10:49 | ||||
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| Collection: | Design and Analysis of Experiments | ||||
| Publisher: | Isaac Newton Institute | ||||
| Copyright: | Sahu, S | ||||
| Language: | eng (English) | ||||
| Distribution: |
World
(downloadable)
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| Credits: |
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| Explicit content: | No | ||||
| Aspect Ratio: | 16:9 | ||||
| Screencast: | No | ||||
| Bumper: | UCS Default | ||||
| Trailer: | UCS Default | ||||
| Abstract: | Modelling data that change over space and time is important in many areas, such as environmental monitoring of air and noise pollution using a sensor network over a long period of time. Often such data are collected dynamically together with the values of a variety of related variables. Due to resource limitations, an optimal choice (or design) for the locations of the sensors is important for achieving accurate predictions. This choice depends on the adopted model, that is, the spatial and temporal processes, and the dependence of the responses on relevant covariates. We investigate adaptive designs for state-space models where the selection of locations at time point tn+1 draws on information gained from observations made at the locations sampled at preceding time points t1,…,tn. A Bayesian design selection criterion is developed and its performance is evaluated using several examples. |
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