Non-Gaussian spatio-temporal processes

On Recently Developed Non-Gaussian Priors and Sampling Methods with Application to Industrial Tomography

Prof. Lassi Roininen, Applied Mathematics, LUT University

Nov 9, 15:00 - 16:00

B1 L4 R4214

Non-Gaussian spatio-temporal processes Bayesian Inversion machine learning

We consider two sets of new priors for Bayesian inversion and machine learning: The first one is based on mixture of experts models with Gaussian processes. The target is to estimate the number of experts and their parameters, and to make state estimation. For sampling, we use SMC^2. For non-Gaussian priors, we discuss Cauchy priors and the generalisation to high-order Cauchy fields and further generalisation to alpha-stable fields. For sampling, we use a selection of modern MCMC tools. Finally, we apply some of the methods and models to an industrial tomography problem on estimating log internal structure, measured at sawmills, based on X-ray, RGB camera and laser scanning.

Yuan Yan

Ph.D. Student, Statistics

Non-Gaussian spatio-temporal processes data transformations wind emulation and forecasting depth for functional data

Yuan Yan is presently working as a Postdoc at Dalhousie University, Halifax, Canada Education: B.A. in Computing Mathematics, 2012, City University of Hong Kong M.S ., 2014, King Abdullah University of Science and Technology Ph.D. ., 2018, King Abdullah University of Science and Technology Research Interests Non-Gaussian spatio-temporal processes, data transformations, wind emulation and forecasting, depth for functional data.