Bayesian methodsBayesian / computational
多层变分推断
多层变分推断（MLVI）是一种可扩展的近似贝叶斯方法，它通过优化后验的变分近似而不是抽取MCMC样本来拟合分层（多层）模型。它利用多层数据的分组结构——个体嵌套在组内，组嵌套在更高级别的单元内——来推导高效的坐标更新，从而使贝叶斯推断对于大型聚类数据集变得可行。
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来源
- Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859-877. DOI: 10.1080/01621459.2017.1285773 ↗
- Ranganath, R., Altosaar, J., Tran, D., & Blei, D. M. (2016). Operator variational objectives. Advances in Neural Information Processing Systems, 29. Curran Associates. link ↗
如何引用本页
ScholarGate. (2026, June 3). Multilevel Variational Inference for Hierarchical Bayesian Models. ScholarGate. https://scholargate.app/zh/bayesian/multilevel-variational-inference
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