Name
Abstract | ||
|---|---|---|
Likelihood-free methods perform parameter inference in stochastic simulator models where evaluating the likelihood is intractable but sampling synthetic data is possible. One class of methods for this likelihood-free problem uses a classifier to distinguish between pairs of parameter-observation samples generated using the simulator and pairs sampled from some reference distribution, which implicitly learns a density ratio proportional to the likelihood. Another popular class of methods fits a conditional distribution to the parameter posterior directly, and a particular recent variant allows for the use of flexible neural density estimators for this task. In this work, we show that both of these approaches can be unified under a general contrastive learning scheme, and clarify how they should be run and compared. |
Year | Venue | DocType |
|---|---|---|
2020 | ICML | Conference |
Volume | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Conor Durkan | 1 | 0 | 1.69 |
| Murray Iain | 2 | 0 | 0.68 |
| George Papamakarios | 3 | 25 | 5.25 |