Parameter inference, model error and the goals of calibration
39 mins 38 secs,
151.61 MB,
iPod Video
480x270,
29.97 fps,
44100 Hz,
522.29 kbits/sec
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About this item
| Description: |
Williamson, D
Wednesday 11th April 2018 - 11:30 to 12:00 |
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| Created: | 2018-04-11 15:07 |
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| Collection: | Uncertainty quantification for complex systems: theory and methodologies |
| Publisher: | Isaac Newton Institute |
| Copyright: | Williamson, D |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | I have some data, a mathematical model describing a process in the real world that produced that data and I would like to learn something about the real world. We would typically formulate this as an inverse problem and apply our favourite techniques for solving it (e.g. Bayesian calibration or history matching), ultimately providing inference for those parameters in our mathematical model that are consistent with the data. Does this make sense? In this talk, I will use climate science as a lens through which we can look at how mathematical models are viewed and treated by the scientific community, and consider UQ approaches to inverse problems and how they might fit and ask whether it matters if they don't. |
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| MPEG-4 Video | 640x360 | 1.93 Mbits/sec | 576.40 MB | View | Download | |
| WebM | 640x360 | 741.94 kbits/sec | 215.47 MB | View | Download | |
| iPod Video * | 480x270 | 522.29 kbits/sec | 151.61 MB | View | Download | |
| MP3 | 44100 Hz | 249.81 kbits/sec | 72.58 MB | Listen | Download | |
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