modeling stars and markets

Andy Casey (Cambridge) arrived in NYC for two weeks to work on a compressed-sensing version of The Cannon. We talked about the design matrix, which is built from functions of the stellar labels. We also talked about how to do the cross-validation to both set the hyper-parameters (which are regularization strengths) and also check the prediction accuracy, without double-using the data. We are adopting ideas from Foreman-Mackey, who also encouraged us to think about propagating label uncertainties. We keep deciding this is computationally impossible, but Foreman-Mackey had some cheap-and-dirty suggestions (which come at the cost of severe approximation).

In the afternoon, Tim Geithner (formerly US Secretary of the Treasury) came by the Simons Foundation to talk about his career and involvement in the 2007–2009 financial crisis. This isn't really bloggable (according to The Rules at right), except that he actually used the word “Bayesian” to describe the attitude that he and his colleagues at the Federal Reserve (where he was in 2007 and 2008) took towards global financial markets, where there is no firmly established causal model. They had to act (or not act) in the face of great uncertainty. I am not sure I agree with what they did, but he quasi-espoused a quasi-principle that I hadn't heard before: Instead of optimizing expected utility, they tried to optimize for ability to correct their mistakes after the fact. It is like expected repair-ability! Not sure if that is really a useful guiding principle, but it occurred to me that it might have implications for policies around global climate change. And TESS and LSST?

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