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This paper introduces a fully Bayesian framework for point cloud curve reconstruction using Markov chain Monte Carlo algorithms, which handles noise and missing data while providing uncertainty quantification, with experiments on synthetic and real-world LiDAR data showing accurate results.
This paper presents PC-MCMC-CIGP, a gray-box workflow that combines spike-and-slab topology sampling with physical constraints and a Chemical-Informed Gaussian Process for reaction network discovery. The method demonstrates improved yield on styrene epoxidation and distinguishes elementary pathways from deceptive fits on a hydrogen-bromine benchmark.
This paper develops a scaling limit theory for SGLD-Gibbs to provide principled hyperparameter tuning guidance for meaningful uncertainty quantification in large-scale latent variable models.
This paper proposes new discrete-time approximations for stochastic gradient Langevin dynamics (SGLD) with and without momentum, enabling accurate predictions of stationary covariance, iterate average covariance, and integrated autocorrelation time. The method provides improved tuning guidance for large-sample uncertainty quantification, especially under model misspecification.