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

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  • Qiao, Xinghao
  • Liu, Yirui
  • Lam, Jessica

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.

Suggested Citation

  • Qiao, Xinghao & Liu, Yirui & Lam, Jessica, 2022. "CATVI: conditional and adaptively truncated variational inference for hierarchical Bayesian nonparametric models," LSE Research Online Documents on Economics 114639, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:114639
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    References listed on IDEAS

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    5. Ghosal,Subhashis & van der Vaart,Aad, 2017. "Fundamentals of Nonparametric Bayesian Inference," Cambridge Books, Cambridge University Press, number 9780521878265.
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    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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