Adversarial Likelihood-Free Inference on Black-Box Generator
This work addresses parameter estimation for black-box generators, which is incremental as it builds on existing likelihood-free inference methods by mitigating their analyzed limitations.
The paper tackled the problem of estimating input parameters for black-box generative models by analyzing limitations in existing likelihood-free inference methods and introducing a new algorithm, Adversarial Likelihood-Free Inference (ALFI). The result showed that ALFI achieved the best parameter estimation accuracy with a limited simulation budget in experiments with diverse simulation and pre-trained statistical models.
Generative Adversarial Network (GAN) can be viewed as an implicit estimator of a data distribution, and this perspective motivates using the adversarial concept in the true input parameter estimation of black-box generators. While previous works on likelihood-free inference introduces an implicit proposal distribution on the generator input, this paper analyzes theoretic limitations of the proposal distribution approach. On top of that, we introduce a new algorithm, Adversarial Likelihood-Free Inference (ALFI), to mitigate the analyzed limitations, so ALFI is able to find the posterior distribution on the input parameter for black-box generative models. We experimented ALFI with diverse simulation models as well as pre-trained statistical models, and we identified that ALFI achieves the best parameter estimation accuracy with a limited simulation budget.