🤖 AI Summary
This study addresses the challenge of specifying concentration parameter priors in Dirichlet process mixture models, where default hyperpriors often impose overly strong or unintended assumptions. To resolve this, the authors propose the Design Conditional prior Elicitation (DCE) framework, which translates practitioners’ prior beliefs about clustering structure into a Gamma hyperprior tailored to a fixed sample size, thereby jointly regulating the number of clusters and the distribution of mixture weights. The approach employs a two-stage moment-matching procedure to enhance computational efficiency and introduces a dual-anchor protocol to diagnose and mitigate risks of weight dominance. Experiments demonstrate that DCE-calibrated priors substantially reduce posterior collapse rates—by over 60% compared to the default Gamma(1,1)—and consistently improve clustering accuracy and robustness across varying data informativeness. An open-source R package, DPprior, and reproducible diagnostic workflows are provided.
📝 Abstract
Dirichlet process mixture (DPM) models are widely used for semiparametric Bayesian analysis in educational and behavioral research, yet specifying the concentration parameter remains a critical barrier. Default hyperpriors often impose strong, unintended assumptions about clustering, while existing calibration methods based on cluster counts suffer from computational inefficiency and fail to control the distribution of mixture weights. This article introduces Design-Conditional Elicitation (DCE), a unified framework that translates practitioner beliefs about cluster structure into coherent Gamma hyperpriors for a fixed design size J. DCE makes three contributions. First, it solves the computational bottleneck using Two-Stage Moment Matching (TSMM), which couples a closed-form approximation with an exact Newton refinement to calibrate hyperparameters without grid search. Second, addressing the"unintended prior"phenomenon, DCE incorporates a Dual-Anchor protocol to diagnose and optionally constrain the risk of weight dominance while transparently reporting the resulting trade-off against cluster-count fidelity. Third, the complete workflow is implemented in the open-source DPprior R package with reproducible diagnostics and a reporting checklist. Simulation studies demonstrate that common defaults such as Gamma(1, 1) induce posterior collapse rates exceeding 60% regardless of the true cluster structure, while DCE-calibrated priors substantially reduce bias and improve recovery across varying levels of data informativeness.