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Current variational inference methods for hierarchical Bayesian nonparametricmodels can neither characterize the correlation structure among latentvariables due to the mean-field setting, nor infer the true posterior dimensionbecause of the universal truncation. To overcome these limitations, we proposethe conditional and adaptively truncated variational inference method (CATVI)by maximizing the nonparametric evidence lower bound and integrating MonteCarlo into the variational inference framework. CATVI enjoys several advantagesover traditional methods, including a smaller divergence between variationaland true posteriors, reduced risk of underfitting or overfitting, and improvedprediction accuracy. Empirical studies on three large datasets reveal thatCATVI applied in Bayesian nonparametric topic models substantially outperformscompeting models, providing lower perplexity and clearer topic-wordsclustering.

CATVI: Conditional and Adaptively Truncated Variational Inference for Hierarchical Bayesian Nonparametric Models