University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Category theory and functional programming for scalable statistical modelling and computational inference

Category theory and functional programming for scalable statistical modelling and computational inference

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact info@newton.ac.uk.

SINW01 - Scalable statistical inference

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.

This talk is part of the Isaac Newton Institute Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2017 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity