CATVI: conditional and adaptively truncated variational inference for hierarchical Bayesian nonparametric models

Xinghao Qiao, Yirui Liu and Jessica Lam

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Current variational inference methods for hierarchical Bayesian nonparametric models can neither characterize the correlation struc- ture among latent variables due to the mean- eld setting, nor infer the true posterior dimension because of the universal trunca- tion. To overcome these limitations, we pro- pose the conditional and adaptively trun- cated variational inference method (CATVI) by maximizing the nonparametric evidence lower bound and integrating Monte Carlo into the variational inference framework. CATVI enjoys several advantages over tra- ditional methods, including a smaller diver- gence between variational and true posteri- ors, reduced risk of undertting or overt- ting, and improved prediction accuracy. Em- pirical studies on three large datasets re- veal that CATVI applied in Bayesian non- parametric topic models substantially out- performs competing models, providing lower perplexity and clearer topic-words clustering.

JEL-codes: C1 (search for similar items in EconPapers)
Date: 2022-03-01
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Published in Proceedings of Machine Learning Research, 1, March, 2022, 151. ISSN: 2640-3498

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