Modeling space-time data often relies on parametric covariance models and various assumptions such as full symmetry and separability. These assumptions are important because they simplify the structure of the model and its inference, and ease

02 - Questions - Noël Cressie

Jan 11th, 2009 9m 39s

Noël Cressie - Ohio State University Bande son disponible au format mp3 Durée : 10 mn

01 - Predicting spatial exceedance regions - Noël Cressie

Jan 11th, 2009 43m 43s

In geostatistics, a common problem is to predict a spatial exceedance and its exceedance region. This is scientifically important since unusual events tend to strongly impact the environment. Here, we use classes of loss functions based on im

04 - Questions - Marc Genton

Apr 23rd, 2007 4m 38s

Marc Genton - University of Geneva Bande son disponible au format mp3 Durée : 5 mn

05 - Spatio-temporal weather models - Chris Glasbey

Apr 22nd, 2007 50m 22s

We develop contrasting spatio-temporal models for two weather variables: solar radiation and rainfall. For solar radiation the aim is to assess the performance of area networks of photo-voltaic cells. Although radiation measured at a suffic

06 - Questions - Chris Glasbey

Apr 21st, 2007 13m 22s

Chris Glasbey - Biomathematics and Statistics Scotland Bande son disponible au format mp3 Durée : 14 mn

07 - Space-time models for soil moisture dynamics - Valérie Isham

Apr 20th, 2007 47m 53s

Soil moisture provides the physical link between soil, climate and vegetation. It increases via the infiltration of rainfall and decreases through evapotranspiration, run-off and leakage, all these effects being dependent on the existing soil

08 - Questions - Valérie Isham

Apr 19th, 2007 7m 19s

Valérie Isham - University College, London Bande son disponible au format mp3 Durée : 8 mn

09 - Analysis of spacetime patterns of disease risk - Sylvia Richardson

Apr 18th, 2007 56m 16s

The fields of geographical epidemiology and public health surveillance have benefited from combined advances in hierarchical model building and in geographical information systems. Exploring and characterising a variety of spatial patterns of

10 - Questions - Sylvia Richardson

Apr 17th, 2007 13m 5s

Sylvia Richardson - Imperial College, London Bande son disponible au format mp3 Durée : 13 mn

11 - Approximate Bayesian inference for latent Gaussian spatial models - Havard Rue

Apr 16th, 2007 43m 26s

Gaussian models are frequently used within spatial statistics and often as a latent Gaussian model is hierachical formulations. The devellopment of Markov chain Monte Carlo methods also allow for spatial analysis of non-Gaussian observations