linear algebra; huge models

I had a long conversation today with Justin Alsing (Flatiron) about hierarchical Bayesian inference, which he is thinking about (and doing) in various cosmological contexts. He is thinking about inferring a density field that simultaneously models the galaxy structures and the weak lensing, to do a next-generation (and statistically sound) lensing tomography. His projects are amazingly sophisticated, and he is not afraid of big models. We also talked about using machine learning to do emulation of expensive simulations, initial-conditions reconstruction in cosmology, and moving-object detection in imaging.

I also spent time playing with my linear algebra expressions for my document on finding identical stars. Some of the matrices in play are low-rank; so I ought to be able to either simplify my expressions or else simplify the number of computational steps. Learning about my limitations, mathematically! One thing I re-discovered today is how useful it is to use the Kusse & Westwig notation and conceptual framework for thinking about hermitian matrices and linear algebra.

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