Sequential Likelihood-Free Inference with Neural Proposal

Dongjun Kim, Kyungwoo Song, Yoon Yeong Kim, Yongjin Shin, Wanmo Kang, Il Chul Moon, Weonyoung Joo

Research output: Contribution to journalArticlepeer-review


Bayesian inference without the likelihood evaluation, or likelihood-free inference, has been a key research topic in simulation studies for gaining quantitatively validated simulation models on real-world datasets. As the likelihood evaluation is inaccessible, previous papers train the amortized neural network to estimate the ground-truth posterior for the simulation of interest. Training the network and accumulating the dataset alternatively in a sequential manner could save the total simulation budget by orders of magnitude. In the data accumulation phase, the new simulation inputs are chosen within a portion of the total simulation budget to accumulate upon the collected dataset so far. This newly accumulated data degenerates because the set of simulation inputs is hardly mixed, and this degenerated data collection process ruins the posterior inference. This paper introduces a new sampling approach, called Neural Proposal (NP), of the simulation input that resolves the biased data collection as it guarantees the i.i.d. sampling. The experiments show the improved performance of our sampler, especially for the simulations with multi-modal posteriors.

Original languageEnglish
Pages (from-to)102-109
Number of pages8
JournalPattern Recognition Letters
StatePublished - May 2023


  • Generative models
  • Likelihood-Free inference
  • MCMC
  • Simulation parameter calibration


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