Name
Abstract | ||
|---|---|---|
Likelihood-free inference refers to inference when a likelihood function cannot be explicitly evaluated, which is often the case for models based on simulators. Most of the literature is based on sample-based `Approximate Bayesian Computationu0027 methods, but recent work suggests that approaches based on deep neural conditional density estimators can obtain state-of-the-art results with fewer simulations. The neural approaches vary in how they choose which simulations to run and what they learn: an approximate posterior or a surrogate likelihood. This work provides some direct controlled comparisons between these choices. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Machine Learning | Journal |
Volume | Citations | PageRank |
abs/1811.08723 | 0 | 0.34 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Conor Durkan | 1 | 0 | 1.69 |
| George Papamakarios | 2 | 25 | 5.25 |
| Iain Murray | 3 | 12 | 2.62 |