Uncertainty quantification for Geo-spatial process
54 mins 41 secs,
100.03 MB,
MP3
44100 Hz,
249.75 kbits/sec
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About this item
| Description: |
Carey, M
Tuesday 20th March 2018 - 13:30 to 14:30 |
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| Created: | 2018-03-21 14:42 |
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| Collection: | Statistical scalability |
| Publisher: | Isaac Newton Institute |
| Copyright: | Carey, M |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
|
| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Co-author: James Ramsay (Prof)
Geo spatial data are observations of a process that are collected in conjunction with reference to their geographical location. This type of data is abundant in many scientific fields, some examples include: population census, social and demographic (health, justice, education), economic (business surveys, trade, transport, tourism, agriculture, etc.) and environmental (atmospheric and oceanographic) data. They are often distributed over irregularly shaped spatial domains with complex boundaries and interior holes. Modelling approaches must account for the spatial dependence over these irregular domains as well as describing there temporal evolution. Dynamic systems modelling has a huge potential in statistics, as evidenced by the amount of activity in functional data analysis. Many seemingly complex forms of functional variation can be more simply represented as a set of differential equations, either ordinary or partial. In this talk, I will present a class of semi parametric regression models with differential regularization in the form of PDEs. This methodology will be called Data2PDE “Data to Partial Differential Equations". Data2PDE characterizes spatial processes that evolve over complex geometries in the presence of uncertain, incomplete and often noisy observations and prior knowledge regarding the physical principles of the process characterized by a PDE. |
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