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By choosing a species sampling random probability measure for the distribution of the basis coefficients, a general class of nonparametric Bayesian methods for clustering of functional data is developed. Allowing the basis functions to be unknown, one faces the problem of posterior simulation over a high-dimensional space of semiparametric models. To address this problem, we propose a novel Metropolis-Hastings...
Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables. In this article, we propose a broad class of semiparametric...
Summary: Dynamic latent class models provide a flexible framework for studying biologic processes that evolve over time. Motivated by studies of markers of the fertile days of the menstrual cycle, we propose a discrete‐time dynamic latent class framework, allowing change points to depend on time, fixed predictors, and random effects. Observed data consist of multivariate categorical indicators, which...
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