MM-SOLD is a training-free particle sampler whose large-particle limit converges to a moment-matched Gibbs distribution obtained by exponentially tilting a score-smoothed target.
Chikuse, Statistics on Special Manifolds, Lecture Notes in Statistics
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MSFAST extends the FAST FPCA method to multivariate sparse data via Bayesian modeling with orthonormal splines, standardization, Procrustes alignment, and efficient computation, yielding valid inferences especially in low signal-to-noise settings.
No additional conditions beyond the spline projection and mixed-effects equivalence are needed for the smoothing prior and posterior to be proper in fully-Bayesian FPCA.
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Training-Free Generative Sampling via Moment-Matched Score Smoothing
MM-SOLD is a training-free particle sampler whose large-particle limit converges to a moment-matched Gibbs distribution obtained by exponentially tilting a score-smoothed target.
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Bayesian Multivariate Sparse Functional Principal Components Analysis
MSFAST extends the FAST FPCA method to multivariate sparse data via Bayesian modeling with orthonormal splines, standardization, Procrustes alignment, and efficient computation, yielding valid inferences especially in low signal-to-noise settings.
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Sufficient conditions for proper posteriors in fully-Bayesian Functional PCA
No additional conditions beyond the spline projection and mixed-effects equivalence are needed for the smoothing prior and posterior to be proper in fully-Bayesian FPCA.
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