Deep Fiducial Inference
This work addresses a computational bottleneck in statistical inference for researchers in fields requiring complex fiducial methods, representing an incremental improvement over existing MCMC-based approaches.
The paper tackles the computational challenge of extracting generalized fiducial distributions in complex scenarios by proposing a fiducial autoencoder (FAE) combined with an approximate fiducial computation (AFC) algorithm, resulting in effective inverse solutions and excellent coverage performance as shown in numerical experiments.
Since the mid-2000s, there has been a resurrection of interest in modern modifications of fiducial inference. To date, the main computational tool to extract a generalized fiducial distribution is Markov chain Monte Carlo (MCMC). We propose an alternative way of computing a generalized fiducial distribution that could be used in complex situations. In particular, to overcome the difficulty when the unnormalized fiducial density (needed for MCMC), we design a fiducial autoencoder (FAE). The fitted autoencoder is used to generate generalized fiducial samples of the unknown parameters. To increase accuracy, we then apply an approximate fiducial computation (AFC) algorithm, by rejecting samples that when plugged into a decoder do not replicate the observed data well enough. Our numerical experiments show the effectiveness of our FAE-based inverse solution and the excellent coverage performance of the AFC corrected FAE solution.