A Bayesian posterior sampler (GMM prior × classifier validity-likelihood, sampled with NUTS) generates heart-valve shapes in POD coefficient space, outperforming PCA-based statistical shape models on validity and coverage in low-data regimes.
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Bayesian Posterior Sampling for Synthetic Shape Generation of Heart Valves
A Bayesian posterior sampler (GMM prior × classifier validity-likelihood, sampled with NUTS) generates heart-valve shapes in POD coefficient space, outperforming PCA-based statistical shape models on validity and coverage in low-data regimes.