Editing Importance sampling (section)

== Application to probabilistic inference ==

Such methods are frequently used to estimate posterior densities or expectations in state and/or parameter estimation problems in probabilistic models that are too hard to treat analytically. Examples include [[Bayesian network]]s and importance weighted [[variational autoencoder]]s.<ref>{{Cite journal |last1=Burda |first1=Yuri |last2=Grosse |first2=Roger |last3=Salakhutdinov |first3=Ruslan |title=Importance Weighted Autoencoders |journal=Proceedings of the 4th International Conference on Learning Representations (ICLR) |arxiv=1509.00519 |publication-date=2016}}</ref>