Maximum-Entropy Priors with Derived Parameters in a Specified Distribution
classification
🧮 math.ST
astro-ph.COastro-ph.IMhep-phstat.TH
keywords
distributionparametersspecifiedapplicableapproachchoicederiveddistributions
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We propose a method for transforming probability distributions so that parameters of interest are forced into a specified distribution. We prove that this approach is the maximum entropy choice, and provide a motivating example applicable to neutrino hierarchy inference.
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Cited by 1 Pith paper
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Planck 2018 results. VI. Cosmological parameters
Final Planck CMB data confirms the flat 6-parameter ΛCDM model with Ω_c h² = 0.120 ± 0.001, Ω_b h² = 0.0224 ± 0.0001, n_s = 0.965 ± 0.004, τ = 0.054 ± 0.007, H_0 = 67.4 ± 0.5 km/s/Mpc, and no strong evidence for extensions.
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