Stephen Roberts is the Royal Academy of Engineering/Man Group Professor of Machine Learning at the University of Oxford, Professorial Fellow of Somerville College, Oxford, and co-founder of the Machine Learning spin-out company, Mind Foundry. He is a Fellow of the Royal Academy of Engineering, the Royal Statistical Society, the IET, and ELLIS.eu. Stephen’s focus lies in the theory and methodology of machine learning for real-world problems, especially those in which noise and uncertainty abound. He has successfully applied these approaches to a wide range of problem domains including astronomy, biology, finance, engineering, control, sensor networks and system monitoring. His current interests include the application of machine learning in physics, ecological monitoring, finance and the engineering industry as well as a range of theoretical and methodological problems.
In this paper, the authors propose to approach the calibration problem of local volatility with Bayesian statistics to infer a conditional distribution over functions given observed data.