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| Inferencia Variacional Jeràrquica× | Cadenes de Markov Monte Carlo (MCMC)× | |
|---|---|---|
| Camp | Bayesià | Bayesià |
| Família | Bayesian methods | Bayesian methods |
| Any d'origen≠ | 2016 | — |
| Autor original≠ | Ranganath, Altosaar, Tran & Blei | — |
| Tipus≠ | Bayesian approximate inference | Posterior sampling algorithm |
| Font seminal≠ | 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 |
| Àlies≠ | HVI, hierarchical variational models, hierarchical VI, hierarchical approximate inference | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Relacionats≠ | 5 | 3 |
| Resum≠ | 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. | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. |
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