Presents a linear PDF parametrization from dimensionality-reduced neural network bases for efficient Bayesian inference, tested via multi-closure tests on synthetic deep inelastic scattering data.
Collaborative Nested Sampling: Big Data versus Complex Physical Models
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dynesty is an open-source Python package for dynamic nested sampling that improves efficiency in Bayesian posterior and evidence estimation compared to MCMC on certain problems.
A multi-bin simultaneous dipole fitting method yields higher Bayes factors than single flux-cut approaches for non-power-law luminosity functions in cosmic dipole measurements.
Betti curves from persistent homology of large-scale structure provide complementary cosmological constraints on ns, sigma8, and Om, with tighter bounds when analyzed jointly with the power spectrum.
citing papers explorer
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A linear PDF model for Bayesian inference
Presents a linear PDF parametrization from dimensionality-reduced neural network bases for efficient Bayesian inference, tested via multi-closure tests on synthetic deep inelastic scattering data.
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dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences
dynesty is an open-source Python package for dynamic nested sampling that improves efficiency in Bayesian posterior and evidence estimation compared to MCMC on certain problems.
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The Ellis and Baldwin test of the Cosmic Dipole: Exploring the impact of multiple flux density cuts
A multi-bin simultaneous dipole fitting method yields higher Bayes factors than single flux-cut approaches for non-power-law luminosity functions in cosmic dipole measurements.
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Counting voids and filaments: Betti Curves as a Powerful Probe for Cosmology
Betti curves from persistent homology of large-scale structure provide complementary cosmological constraints on ns, sigma8, and Om, with tighter bounds when analyzed jointly with the power spectrum.