Sparse Log Gaussian Processes via MCMC for Spatial Epidemiology
author:
Jarno Vanhatalo,
Helsinki University of Technology
Description
Log Gaussian processes (LGP) are an attractive manner to construct intensity surfaces for the purposes of spatial epidemiology. The intensity surfaces are naturally smoothed by placing a GP prior over the relative log Poisson rate. In this work a fully independent training conditional (FITC) sparse approximation is used to speed up GP computations. The sampling of the latent values is sped up with transformations taking into account the approximate conditional posterior precision.
You might be experiencing some problems with Your Video player.
SEE ALSO:
Download slides:
gpip06_vanhatalo_slgpv_01.pdf (188.8 KB)
Launch in a standalone WM PlayerSwitch to Windows Media Player
Streaming Video Help
Windows Media Player Firefox Plugin - Download
Lecture rating
| People found this lecture: | ||
| Worth seeing | ||
| because it is: | ||
| Valuable and informative | ||
| Well presented | ||
| Easily understandable | ||
| Acceptably recorded | ||
| You need to login to cast your vote. | ||
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Related content
Visitors who watched this lecture also watched...
37 comments
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
