Material relating to my ISBA 2021 presentation
- Talk: video, slides, markdown
- A longer (2 hour) talk (and slides) covering similar ideas, but in more detail, with code examples (in Scala)
- Law, J. & Wilkinson, D.J. (2019) Functional probabilistic programming for scalable Bayesian modelling, in submission.
- Law, J. & Wilkinson, D.J. (2018) Composable models for online Bayesian analysis of streaming data, Statistics and Computing, 28(6): 1119-1137.
- Fong, B., Spivak, D., Tuyéras, R. (2019) Backprop as Functor: A compositional perspective on supervised learning, arXiv, 1711.10455.
- Heunen, C., Kammar, O., Staton, S., Yang, H. (2017) A convenient category for higher-order probability theory, 32nd ACM/IEEE LICS., 1–12.
- Ścibior, A., Kammar, O., Gharamani, Z. (2018) Functional programming for modular Bayesian inference, Proc. ACM Prog. Lang., 2(ICFP): 83.
- van der Meulen, F., Schauer, M. (2021) Automatic Backward Filtering Forward Guiding for Markov processes and graphical models, arXiv, 2010.0350.
- List of papers on category theory for machine learning
- Scala - modern, strict functional language
- Haskell - old, lazy functional language
- OCaml - interesting language with good FP support
- Rust - not functional, but interesting anyway
- Dex - experimental FP language for differentiable array programming, with ML applications in mind
- My website(s)
- Scala for statistical computing and data science free online course
- Some category theory for FP links
- My blog - relevant posts include: