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f(x) = y, but y = 5. What's the probability that x =1? Probabilistic programming is the idea of describing probabilistic models as programs, to then automatically infer how our beliefs about model parameters change given observed data. In recent years, probabilistic programming languages (such as Stan) have demonstrated the power of this approach by becoming the underlying tool behind numerous projects in social science, biology, genetics, astrophysics, and engineering. But why haven't such languages been more widely adopted yet? In this talk, Maria will talk about the nuts and bolts of probabilistic programming languages, addressing the challenges behind making these languages general-purpose, automatic, and efficient. In addition, she will discuss how ideas from programming-language research can be adopted in probabilistic systems, and how F# has helped her in the endeavour of bridging the gap between statistical modelling and probabilistic inference.
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Keynote: Probabilistic Programming: The What, Why and How
Maria is a Data Science PhD student at the University of Edinburgh, where she works on improving the expressivity and efficiency of probabilistic programming languages. In particular, she is interested in applying program-analysis techniques to existing probabilistic languages, such as Stan. Previously, Maria worked as a Research Assistant in the Graphics and Interaction Group at the University of Cambridge, where she also received her BA, developing an interactive development environment for probabilistic programming for her final year project.