Category theory and functional programming for scalable statistical modelling and computational inference
45 mins 38 secs,
664.71 MB,
MPEG-4 Video
640x360,
29.97 fps,
44100 Hz,
1.94 Mbits/sec
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About this item
| Description: |
Wilkinson, D
Tuesday 4th July 2017 - 16:15 to 17:00 |
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| Created: | 2017-07-21 14:22 |
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| Collection: | Scalable inference; statistical, algorithmic, computational aspects |
| Publisher: | Isaac Newton Institute |
| Copyright: | Wilkinson, 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: | This talk considers both the theoretical and computational requirements for scalable statistical modelling and computation. It will be argued that programming languages typically used for statistical computing do not naturally scale, and that functional programming languages by contrast are ideally suited to the development of scalable statistical algorithms. The mathematical subject of category theory provides the necessary theoretical underpinnings for rigorous analysis and reasoning about functional algorithms, their correctness, and their scalability. Used in conjunction with other tools from theoretical computer science, such as recursion schemes, these approaches narrow the gap between statistical theory and computational implementation, providing numerous benefits, not least automatic parallelisation and distribution of algorithms. |
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| MPEG-4 Video * | 640x360 | 1.94 Mbits/sec | 664.71 MB | View | Download | |
| WebM | 640x360 | 669.06 kbits/sec | 223.70 MB | View | Download | |
| iPod Video | 480x270 | 522.35 kbits/sec | 174.59 MB | View | Download | |
| MP3 | 44100 Hz | 249.76 kbits/sec | 83.57 MB | Listen | Download | |
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