Professor Doug Nychka from NCAR will be visiting the School of Mathematics and Statistics, University of Glasgow as the 2014 Mitchell lecturer from 5-9th May. He will give two talks
Tuesday 6th May, 3pm room 203, Mathematics building
Uncertain weather, uncertain climate
What will the weather be tomorrow? How cold was it 500 years ago?
The first question has a clear relevance to our daily lives and the second is necessary to understand variation in our Earth's climate. Answers to both of these questions rely on statistical methods that combine observations with geophysical models to understand our physical environment. Annual temperatures many centuries in the past can be estimated without the benefit of having direct measurements from thermometers. Should we be skeptical of scientific attempts to do this? This lecture will present statistical methods for scientific problems where observational information is limited and characterizing the uncertainty in the results is important. These methods, known as Bayesian hierarchical models, have become a mainstay of data analysis for complex problems and besides being used in the geosciences have wide application in other areas of science.
This talk will be followed by a welcoming reception in the common room, you are welcome to attend, then please email [log in to unmask] so that we can confirm numbers for catering.
The second talk
Thurs 8th may 3pm, room 203, Mathematics Building
Multi-resolution spatial methods for large data sets.
Spatial data is ubiquitous and a basic problem is to reconstruct surfaces from irregular observations or measurements and to quantify the uncertainty in the estimates. Standard statistical methods break when applied to large data sets and so alternative approaches are needed that balance changes to the statistical models for increases in computational efficiency. A useful method expands the field in a set of compact basis functions and places a Gaussian Markov random field latent model on the basis coefficients. The impact is that evaluating the model likelihood and computing spatial predictions is feasible even for tens of thousands of spatial observations on a single computational core (e.g. a laptop). Moreover, by varying the support of the basis functions and the correlations among basis coefficients it is possible to entertain multi-resolution and nonstationary spatial models that exploit the rich structure often found in large data sets.
Cheers
Marian
E Marian Scott OBE, FRSE, CStat, PhD
Professor of Environmental Statistics
School of Mathematics and Statistics
University of Glasgow
15 University Gardens
Glasgow G12 8QW
tel 0141 330 5125
fax 0141 330 4814
The University of Glasgow, charity number SC004401
You may leave the list at any time by sending the command
SIGNOFF allstat
to [log in to unmask], leaving the subject line blank.
|