shapelets for denoising, theorem, inflation

Vakili and I discussed the issue that you can run kernel PCA on galaxy images, or on the shapelet transforms of galaxy images, and you should get the same answer. PCA is invariant to rotations of the coordinate system. However, really we are using the shapelets for denoising: We truncate the high-order terms that we think are noise-dominated. We discussed less heuristic approaches to this.

At MCMC meeting, Hou showed his impressive results on marginalized likelihood computations. He gets answers that are provably (if the code is correct) unbiased and come with uncertainty estimates. He gets some discrepancies with numbers in the literature, even when he uses the same data and the same prior pdfs, so we are confused, but we don't know how to diagnose the differences. Goodman explained to us the magical Bernstein–von Mises theorem, which guarantees that the posterior pdf approaches a Gaussian as the data grows very large. Of course the theorem depends on assumptions that cannot possibly be true, like that the model space includes the process that generated the data in the first place!

On the phone with the exoSAMSI crew, we de-scoped our first papers on search to the minimum (and set Spring targets for completion). At lunch, Mark Wyman (NYU) talked about modifications to inflation that would make the gravitational wave signatures both more prominent and more informative.

1 comment:

  1. If by results in the literature you mean standard statistical datasets then sometimes you need to be careful which version of the "standard" dataset you use for benchmarking. E.g. with the "galaxy dataset" there's a version with Chib's transcription error in the 78th galaxy and a version without!