Amortized Monte Carlo integration
Current approaches to amortizing Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is, in turn, used to calculate expectations for one or more target functions{—}a computational pipeline which is inefficient when the target function(s) are kno...
Main Authors: | Goliński, A, Wood, F, Rainforth, T |
---|---|
Format: | Conference item |
Published: |
Proceedings of Machine Learning Research
2019
|
Similar Items
-
Faithful inversion of generative models for effective amortized inference
by: Webb, S, et al.
Published: (2019) -
On nesting Monte Carlo estimators
by: Rainforth, T, et al.
Published: (2019) -
Auto-encoding sequential Monte Carlo
by: Le, T, et al.
Published: (2018) -
Amortized rejection sampling in universal probabilistic programming
by: Naderiparizi, S, et al.
Published: (2022) -
The Difference between Accounting Amortization and Fiscal Amortization
by: Florescu Nicu, et al.
Published: (2015-06-01)