Introduces pseudo-marginal MCMC with unbiased Poisson likelihood estimator for exact inference despite noisy collider Monte Carlo simulations.
A Noisy Monte Carlo Algorithm
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abstract
We propose a Monte Carlo algorithm to promote Kennedy and Kuti's linear accept/reject algorithm which accommodates unbiased stochastic estimates of the probability to an exact one. This is achieved by adopting the Metropolis accept/reject steps for both the dynamical and noise configurations. We test it on the five state model and obtain desirable results even for the case with large noise. We also discuss its application to lattice QCD with stochastically estimated fermion determinants.
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hep-ph 1years
2025 1verdicts
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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.