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Mixture Modeling without Training Data
Label switching can be revealed by a multi-model posterior distribution for one or
more parameters. The preceding trace plot corresponds to the following posterior
distribution estimate.
The preceding graph shows that the mean of a parameter’s posterior distribution may
not be a meaningful estimate in a mixture modeling analysis when label switching
occurs. Some methods for preventing label switching have been proposed (Celeux,
Hurn, and Robert, 2000; Frühwirth-Schnatter, 2004; Jasra, Holmes, and Stephens,
2005; Stephens, 2000). Chung, Loken, and Schafer (2004) suggest that pre-assigning
even one or two cases to groups can be effective in eliminating label switching. Amos
allows pre-assigning cases to groups, as shown in Example 34. Amos 21 does not
implement any other method for preventing label switching.