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- W117788235 abstract "A common problem in astrophysics is how to efficiently estimate key parameters for objects when given a very large data set. In his paper, Jimenez describes an approach to data compression that can massively accelerate likelihood computations without losing information about the parameters of interest. The method is known as MOPED (Heavens et al. Mon Not R Astron Soc 317(4):965–972, 2000) and has previously been used for the estimation of star formation history from galaxy spectra (Panter et al. Mon Not R Astron Soc 343(4):1145–1154, 2003; Panter et al. Mon Not R Astron Soc 378(4):1550–1564, 2007), and identification of planetary transits from light curves (Protopapas et al. Mon Not R Astron Soc 362(2):460–468, 2005). Here we discuss the set-up and some of the assumptions of the MOPED approach. We then describe a few alternative or complementary methods of compression and parameter estimation when these assumptions are violated, and discuss their pros and cons." @default.
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- W117788235 date "2012-01-01" @default.
- W117788235 modified "2023-10-14" @default.
- W117788235 title "Commentary: Data Compression Methods in Astrophysics" @default.
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- W117788235 doi "https://doi.org/10.1007/978-1-4614-3520-4_30" @default.
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