Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Automatische Differentiatie Variational Inference (ADVI)× | Expectation Propagation (EP)× | |
|---|---|---|
| Vakgebied | Bayesiaanse statistiek | Bayesiaanse statistiek |
| Familie | Bayesian methods | Bayesian methods |
| Jaar van ontstaan≠ | 2017 | 2001 |
| Grondlegger≠ | Kucukelbir, Tran, Ranganath, Gelman, Blei | Thomas P. Minka |
| Type≠ | Variational inference algorithm | Approximate inference algorithm |
| Oorspronkelijke bron≠ | Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A. & Blei, D. M. (2017). Automatic differentiation variational inference. Journal of Machine Learning Research, 18(14), 1–45. link ↗ | Minka, T. P. (2001). Expectation propagation for approximate Bayesian inference. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI-01), pp. 362–369. Morgan Kaufmann. link ↗ |
| Aliassen | ADVI, black-box variational inference, automatic variational inference, gradient-based variational inference | EP, expectation propagation, EP algorithm, assumed-density filtering generalisation |
| Verwant | 3 | 3 |
| Samenvatting≠ | Automatic Differentiation Variational Inference (ADVI) is a black-box algorithm for approximate Bayesian posterior inference, introduced by Kucukelbir, Tran, Ranganath, Gelman, and Blei (2017, JMLR). Given any probabilistic model whose log-joint density is differentiable, ADVI automatically transforms constrained latent variables to unconstrained real space, fits a Gaussian variational family by maximising the evidence lower bound (ELBO) with stochastic gradient ascent, and returns an approximate posterior without model-specific derivations. It is the default variational inference engine in Stan and is available in PyMC and NumPyro. | Expectation Propagation (EP) is a deterministic message-passing algorithm for approximate posterior inference in Bayesian models, introduced by Thomas P. Minka at UAI 2001. It iteratively refines a set of local approximate factors — each drawn from the exponential family — so that their product closely matches the true intractable posterior, achieving higher accuracy than mean-field variational inference on many probabilistic machine learning tasks. |
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