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| Inferensi Variasional Hierarkis× | Metropolis-Hastings Markov Rantai Hirarkis× | |
|---|---|---|
| Bidang | Bayesian | Bayesian |
| Keluarga | Bayesian methods | Bayesian methods |
| Tahun asal≠ | 2016 | 1990 |
| Pencetus≠ | Ranganath, Altosaar, Tran & Blei | Gelfand & Smith (1990), building on Geman & Geman (1984) |
| Tipe≠ | Bayesian approximate inference | Bayesian computational sampler |
| Sumber perintis≠ | 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 ↗ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| Alias | HVI, hierarchical variational models, hierarchical VI, hierarchical approximate inference | hierarchical MCMC, MCMC for multilevel models, Bayesian hierarchical MCMC, multilevel MCMC sampling |
| Terkait≠ | 5 | 6 |
| Ringkasan≠ | 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. | Hierarchical Markov chain Monte Carlo applies MCMC sampling to hierarchical Bayesian models, jointly drawing from the posterior over both observation-level parameters and the hyperparameters that govern them. This allows principled uncertainty propagation across all levels of a multilevel structure, from individuals to groups to population, using algorithms such as Gibbs sampling, Metropolis-Hastings, or Hamiltonian Monte Carlo. |
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