Introduces pseudo-marginal MCMC with unbiased Poisson likelihood estimator for exact inference despite noisy collider Monte Carlo simulations.
Athron, et al., GAMBIT: The Global and Modular Beyond-the-Standard-Model In- ference Tool, Eur
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Develops a Bayesian naturalness measure and evaluates its implications for the Minimal 4D Composite Higgs model and extensions via global fits and collider bounds.
Jarvis-HEP introduces a YAML-based Python framework for composing workflows and performing parameter scans in high-energy physics.
citing papers explorer
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Bring the noise: exact inference from noisy simulations in collider physics
Introduces pseudo-marginal MCMC with unbiased Poisson likelihood estimator for exact inference despite noisy collider Monte Carlo simulations.
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The Landscape of Composite Higgs Models
Develops a Bayesian naturalness measure and evaluates its implications for the Minimal 4D Composite Higgs model and extensions via global fits and collider bounds.
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Jarvis-HEP: A lightweight Python framework for workflow composition and parameter scans in high-energy physics
Jarvis-HEP introduces a YAML-based Python framework for composing workflows and performing parameter scans in high-energy physics.