Error-Guided Likelihood-Free MCMC

2021 International Joint Conference on Neural Networks (IJCNN)（2021）

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摘要

This work presents a novel posterior inference method for models with intractable evidence and likelihood functions. Error-guided likelihood-free MCMC, or EG-LF-MCMC in short, has been developed for scientific applications, where a researcher is interested in obtaining approximate posterior densities over model parameters, while avoiding the need for expensive training of component estimators on f...

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关键词

Training,Monte Carlo methods,Neural networks,Markov processes,Benchmark testing,Data models,Bayes methods

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