AI Seminar: Continuous-discrete smoothing of diffusions
Frank van der Meulen, Associate Professor in Electrical Engineering, Mathematics and Computer Science at Delft University of Technology, the Netherlands.
In this talk I will consider sampling unobserved parts of the path of a discretely observed diffusion generated by a stochastic differential equation. I will explain how this can be done within the framework of guided proposals. It turns out that the problem can be dealt with while avoiding linearisation of the drift and diffusion coefficients. In this sense the proposed method is a refinement of Kalman smoothing. The method is illustrated on a landmark matching problem.
This seminar is a part of the AI Seminar Series organised by SCIENCE AI Centre. The series highlights advances and challenges in research within Machine Learning, Data Science, and AI. Like the AI Centre itself, the seminar series has a broad scope, covering both new methodological contributions, ground-breaking applications, and impacts on society.