This paper appears in the most recent in issue of SIAM Review, and it is simply fantastic. It is well written and clear, and it judiciously limits the scope to fundamental concepts in statistical inverse problems. I recommend this to anyone interested in such ideas — both students and experienced researchers. In fact, I’m giving a lecture this evening to the UQ class on inverse problems, and I plan to both borrow heavily from its presentation and make it required reading for the class.
Here’s the link.
This paper ‘A Taxonomy of Global Optimization Methods Based on Response Surfaces’ by Donald Jones does a fantastic job of explaining the wide variety of options for response surfaces. It’s written with optimization in mind, but his explanation of the methods is much more general. It’s written clearly, and I strongly recommend it for anyone working with response surfaces.
Here’s a link to the paper.
Stay tuned for some updates from SIAM CSE13 — the conference that quadrupled my TODO list.
I’ve heard a few statisticians complain about the rise in popularity (and funding) of research in uncertainty quantification, claiming that it’s all just a rehashing of statistics. And I think they have a point, for the most part. At the very least, those of us working in UQ should be aware of the excellent resources from the statistics literature that address UQ-like problems.
‘Computer Experiments’ by Koehler and Owen (Chapter 9 from ‘Handbook of statistics 13,’ 1996) is one of my personal favorites. It discusses Kriging and least squares surrogate models for expensive computer simulations and a variety of experimental designs. I consider it a must-read for people interested in UQ.
Here’s a PDF.