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Paper: Probability Density Functions for Astronomy
Volume: 521, Astronomical Data Analysis Software and Systems XXVI
Page: 240
Authors: Polsterer, K. L.; Gieseke, F.
Abstract: In many applications in astronomy, uncertainty quantification plays an important role. Probability density functions (PDFs) allow us to quantify the likelihood of results and therefore enable scientist to produce better analysis results. We present a Python package to generate PDFs for regression tasks. Besides providing several functionalities to generate such PDFs, we present a whole tool set for evaluating the quality and visualizing the performance of the generated PDFs. Photometric redshifts are an important measure of distance for various cosmological topics. As spectroscopic redshifts are only available for a very limited set of objects, statistical regression models are helpful to derive estimates based on photometric measurements. We use the example of generating the photometric redshift PDFs of quasars from SDSS(DR7) based on psf magnitudes to present the functionalities of {\ttfamily ProbReg}.
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