Mixed Membership Models (MMMs) are a popular family of latent structure models for complex multivariate data. Instead of forcing each subject to belong to a single cluster, MMMs incorporate a vector of subject-specific weights characterizing partial membership across clusters. With this flexibility come challenges in uniquely identifying,…

Demonstrates the consequences on total misdiagnosis rates of using conventional (fixed) rather than flexible (Bayesian) rules when using the Minnesota Multiphasic Personality Inventory in diagnosis. Concludes that the range of base rates and the separation of distributions of normal and abnormal scores affect the difference in the misdiagnosis…