Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Fiducial Inference   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.

3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
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The categories of this paper are: cond-mat
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: math
28
The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: gr-qc