heteroskedastic GPLVM; search for anomalies

Christina Eilers (MPIA) and I have decided to re-implement the Gaussian Process latent-variable model, with modifications that permit the data to be heteroskedastic (and missing) and the kernel function to be different along different dimensions of the data space. We spent an hour today de-bugging analytic derivatives. We need these, because there is a non-convex optimization as part of that model. We resolved to bring the action to New York and have Foreman-Mackey (Flatiron) help us re-implement everything in george. I was left with homework to write this model down in full generality.

Kate Storey-Fisher (NYU) and I got close to specifying a well-posed problem in our nascent project to find CMB-like anomalies in large-scale structure data. We read this paper by NYU locals about prospects for future surveys, but we want to work with real data if we can. We discussed how a search for anomalies can be cast as a parameter estimation problem. We haven't settled on a methodology, though.

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