A neural-network surrogate for the log-likelihood gradients makes Hamiltonian Monte Carlo trajectories 30 times faster than relative-binning gradients and recovers LVK-consistent posteriors for two binary neutron star mergers.
∂ lnπ(χi) ∂χi = [χi ln (|χi/χmax|)]−1, (45) we arrive at an expression which causes multiple prob- lems for the HMC
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DeepHMC : a deep-neural-network acclerated Hamiltonian Monte Carlo algorithm for binary neutron star parameter estimation
A neural-network surrogate for the log-likelihood gradients makes Hamiltonian Monte Carlo trajectories 30 times faster than relative-binning gradients and recovers LVK-consistent posteriors for two binary neutron star mergers.