A hierarchical Bayesian framework that uses the empirical anti-correlation between AGN variability amplitude and luminosity to infer cosmological parameters from moderate-baseline light curves via importance reweighting.
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An AI-assisted Bayesian framework extracts TMD PDFs from global Drell-Yan data using surrogate models for scalable MCMC sampling.
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.
Hamilton-Jacobi analysis of slow-roll inflation with non-Bekenstein-Hawking entropies yields fitted entropy parameters (δ≈1.1-1.2, α∼10^{-14}, K∼10^{-17}) consistent with ns and r data.
Review of gradient-based and gradient-free methods for parameter point estimation plus profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification in systems biology models.
citing papers explorer
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A hierarchical Bayesian framework for cosmology using Type 1 AGN variability
A hierarchical Bayesian framework that uses the empirical anti-correlation between AGN variability amplitude and luminosity to infer cosmological parameters from moderate-baseline light curves via importance reweighting.
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AI-assisted modeling and Bayesian inference of unpolarized quark transverse momentum distributions from Drell-Yan data
An AI-assisted Bayesian framework extracts TMD PDFs from global Drell-Yan data using surrogate models for scalable MCMC sampling.
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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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Hamilton-Jacobi Approach to Inflationary Scenarios through Extended Entropies: An Observational Perspective
Hamilton-Jacobi analysis of slow-roll inflation with non-Bekenstein-Hawking entropies yields fitted entropy parameters (δ≈1.1-1.2, α∼10^{-14}, K∼10^{-17}) consistent with ns and r data.
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Parameter Estimation and Uncertainty Quantification for Systems Biology Models
Review of gradient-based and gradient-free methods for parameter point estimation plus profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification in systems biology models.