Abstract
Bayesian inversion is at the heart of probabilistic programming and more generally machine learning. Understanding inversion is made difficult by the pointful (kernel-centric) point of view usually taken in the literature. We develop a pointless (kernel-free) approach to inversion. While doing so, we revisit some foundational objects of probability theory, unravel their category-theoretical underpinnings and show how pointless Bayesian inversion sits naturally at the centre of this construction.
Cite
CITATION STYLE
Clerc, F., Danos, V., Dahlqvist, F., & Garnier, I. (2017). Pointless learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10203 LNCS, pp. 355–369). Springer Verlag. https://doi.org/10.1007/978-3-662-54458-7_21
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
