skip to content

Batch sequential experimental designs for computer experiments

Tuesday 6th September 2011 - 15:30 to 16:00
INI Seminar Room 1
Session Title: 
Algorithms for Constructing Experimental Designs
Session Chair: 
S. Reese
Finding optimal designs for computer experiments that are modeled using a stationary Gaussian Stochastic Process (GaSP) model is challenging because optimality criteria are usually functions of the unknown model parameters. One popular approach is to adopt sequential strategies. These have been shown to be very effective when the optimality criterion is formulated as an expected improvement function. Most of these sequential strategies assume observations are taken sequentially one at a time. However, when observations can be taken k at a time, it is not obvious how to implement sequential designs. We discuss the problems that can arise when implementing batch sequential designs and present several strategies for sequential designs taking observations in k-at-a-time batches. We illustrate these strategies with examples.
The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.
Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons