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New method uses neural networks to enhance Hamiltonian Monte Carlo for inference

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Neural Surrogate HMC, a novel method that integrates neural likelihood estimation with Hamiltonian Monte Carlo for simulation-based inference. This approach leverages neural networks to approximate likelihood functions, offering advantages in amortizing computations, providing gradients for Hamiltonian Monte Carlo, and smoothing noisy simulation results. The method was successfully applied to model the heliospheric transport of galactic cosmic rays, enabling efficient inference of latent parameters within the Parker equation. AI

IMPACT This method could improve the efficiency and accuracy of complex simulations and parameter inference in scientific research.

RANK_REASON The cluster contains a research paper detailing a new computational method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method uses neural networks to enhance Hamiltonian Monte Carlo for inference

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The cluster contains a research paper detailing a new computational method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Linnea M Wolniewicz, Peter Sadowski, Claudio Corti ·

    Neural Surrogate HMC: On Using Neural Likelihoods for Hamiltonian Monte Carlo in Simulation-Based Inference

    arXiv:2407.20432v3 Announce Type: replace Abstract: Bayesian inference methods such as Markov Chain Monte Carlo (MCMC) typically require repeated computations of the likelihood function, but in some scenarios this is infeasible and alternative methods are needed. Simulation-based…

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