A Bayesian change-plane regression method uses a misspecified probit-gated surrogate likelihood to enable regular posterior inference for linear-threshold subpopulation boundaries, recovering the hard-threshold target in the vanishing-smoothing limit under a covariate boundary-margin condition.
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A new MFPCA approach for variable domain data is proposed by running univariate variable-domain FPCA on each variable, stacking the scores, and smoothing the empirical covariance matrix over domain length to recover joint eigenfunctions and scores.
FIM-optimized staggered sensor geometry combined with Phy-GAANet using physics-informed features and geometry-aware attention achieves 1.84 mm position error and 3.18 degree orientation error at over 270 Hz in real-world tests, outperforming classical solvers and standard CNNs.
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Bayesian change-plane regression
A Bayesian change-plane regression method uses a misspecified probit-gated surrogate likelihood to enable regular posterior inference for linear-threshold subpopulation boundaries, recovering the hard-threshold target in the vanishing-smoothing limit under a covariate boundary-margin condition.
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Variable Domain Multivariate Functional Principal Component Analysis
A new MFPCA approach for variable domain data is proposed by running univariate variable-domain FPCA on each variable, stacking the scores, and smoothing the empirical covariance matrix over domain length to recover joint eigenfunctions and scores.
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Information-Theoretic Geometry Optimization and Physics-Aware Learning for Calibration-Free Magnetic Localization
FIM-optimized staggered sensor geometry combined with Phy-GAANet using physics-informed features and geometry-aware attention achieves 1.84 mm position error and 3.18 degree orientation error at over 270 Hz in real-world tests, outperforming classical solvers and standard CNNs.