Hierarchical Variational Inference
Hierarchical variational inference (HVI) extends standard variational inference by placing a richer, hierarchical structure on the variational family itself. Instead of using a simple mean-field approximation, HVI introduces auxiliary latent variables that capture dependencies among the main latent variables, yielding tighter evidence lower bounds and more accurate posterior approximations for complex Bayesian models.
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Avoti
- Ranganath, R., Altosaar, J., Tran, D. & Blei, D. M. (2016). Hierarchical Variational Models. Proceedings of the 33rd International Conference on Machine Learning (ICML 2016), PMLR 48, 324-333. link ↗
- Jordan, M. I., Ghahramani, Z., Jaakkola, T. S. & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183-233. DOI: 10.1023/A:1007665907178 ↗
Kā citēt šo lapu
ScholarGate. (2026, June 3). Hierarchical Variational Inference. ScholarGate. https://scholargate.app/lv/bayesian/hierarchical-variational-inference
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Beijesiskā regresijaBajesa metodes↔ compare
- Hierarhiskā Bayesas inferencēšanaBajesa metodes↔ compare
- Hierarchical Markov Chain Monte CarloBajesa metodes↔ compare
- Mārkova ķēžu Montekarlo (MCMC)Bajesa metodes↔ compare
- Variacionālā secinājumiBajesa metodes↔ compare
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