What is it and how to improve it?
3–4pm Tuesday 29 November
Probabilistic programming languages and frameworks, such as Infer.NET, attempt to unify general purpose programming with probabilistic modelling, in order to introduce a more abstract and powerful way of applying probabilistic models to real-world problems. This approach is becoming especially popular for data analytics, as many statistical techniques are more naturally expressed using such languages.
However, the basic concepts of probabilistic programming differ from those of conventional programming, which means that learning such language could be a challenge even for an experienced programmer. In this talk, I will give an introduction to probabilistic programming, and present an IDE for the Infer.NET framework that was built to address some of those conceptual differences and to make the capabilities of probabilistic programming more accessible to students and end-user developers.
Maria Gorinova is a postgraduate research student at the Edinburgh Centre for Doctoral Training in Data Science, investigating probabilistic programming languages and machine learning. Earlier this year Maria presented her work on IDEs for probabilistic programming at CHI, the leading conference on Human-Computer Interaction, and she has also worked at the University of Cambridge on research into visualisation of healthcare data wrangling.