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New Bayesian algorithm identifies interaction kernels in Motsch-Tadmor model

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new sparse Bayesian learning algorithm to identify interaction kernels within the Motsch-Tadmor model. This method uses a variational framework to reformulate kernel identification as a subspace identification problem. The algorithm incorporates informative priors for regularization and uncertainty quantification, demonstrating accuracy and robustness in numerical experiments. AI

RANK_REASON The cluster contains a new academic paper detailing a novel algorithm for a specific scientific model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Jinchao Feng, Sui Tang ·

    A Sparse Bayesian Learning Algorithm for Estimation of Interaction Kernels in Motsch-Tadmor Model

    arXiv:2505.07068v2 Announce Type: replace Abstract: In this paper, we investigate the data-driven identification of asymmetric interaction kernels in the Motsch-Tadmor model based on observed trajectory data. The model under consideration is governed by a class of semilinear evol…

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