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Large scale spatial statistics
Large spatial and spatio-temporal data sets lead to challenging statistical modelling and computational problems. In some cases one can use a low dimensional model, which allows a very large number of observations to be used. Unfortunately, a common situation is that the increased data size is coupled with a desire to perform analysis on finer scales, e.g. in global and regional temperature reconstruction. I will discuss a method for stochastic multiscale modelling via combinations of stochastic PDEs, and how numerical methods for sparse linear systems might be used to construct direct prediction and conditional sampling methods, avoiding the more costly MCMC approaches that are traditionally used to quantify estimation uncertainty.
Date:
28 April 2015, 14:15
Venue:
Atmospheric, Oceanic and Planetary Physics, off Parks Road OX1 3PU
Venue Details:
Dobson Room
Speaker:
Finn Lindgren (University of Bath)
Organising department:
Department of Physics
Organiser:
Andrew Wells (Atmospheric, Oceanic & Planetary Physics, University of Oxford; & Wolfson College)
Organiser contact email address:
andrew.wells@physics.ox.ac.uk
Part of:
Geophysical & Nonlinear Fluid Dynamics Seminars
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Andrew Wells
