A more rational model of categorization

The rational model of categorization (RMC; Anderson, 1990) assumes that categories are learned by cluster-ing similar stimuli together using Bayesian inference. As computing the posterior distribution over all assign-ments of stimuli to clusters is intractable, an approxi-mation algorithm is used. The original algorithm used in the RMC was an incremental procedure that had no guarantees for the quality of the resulting approxima-tion. Drawing on connections between the RMC and models used in nonparametric Bayesian density esti-mation, we present two alternative approximation al-gorithms that are asymptotically correct. Using these algorithms allows the effects of the assumptions of the RMC and the particular inference algorithm to be ex