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

arXiv — cs.LG•Wednesday, December 10, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
  • A new study introduces Neural Surrogate Hamiltonian Monte Carlo (HMC), which leverages neural likelihoods to enhance Bayesian inference methods, particularly Markov Chain Monte Carlo (MCMC). This approach addresses the computational challenges associated with likelihood function evaluations by employing machine learning techniques to streamline the process. The method demonstrates significant advantages, including improved efficiency and robustness in simulations.
  • This development is crucial as it provides a more efficient framework for Bayesian inference, particularly in scenarios where traditional MCMC methods face computational limitations. By integrating neural networks to approximate likelihood functions, researchers can achieve faster convergence and more accurate results, which is essential for complex modeling tasks in various scientific fields.
  • The integration of neural networks into traditional Bayesian methods reflects a broader trend in artificial intelligence, where machine learning techniques are increasingly applied to enhance classical statistical methods. This synergy not only improves computational efficiency but also addresses issues of uncertainty and noise in simulations, aligning with ongoing efforts to refine probabilistic modeling and inference techniques across disciplines.
— via World Pulse Now AI Editorial System
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