Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Invertible Neural Networks versus MCMC for Posterior Reconstruction in
  Grazing Incidence X-Ray Fluorescence   Grazing incidence X-ray fluorescence is a non-destructive technique for
analyzing the geometry and compositional parameters of nanostructures appearing
e.g. in computer chips. In this paper, we propose to reconstruct the posterior
parameter distribution given a noisy measurement generated by the forward model
by an appropriately learned invertible neural network. This network resembles
the transport map from a reference distribution to the posterior. We
demonstrate by numerical comparisons that our method can compete with
established Markov Chain Monte Carlo approaches, while being more efficient and
flexible in applications.

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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: econ
21
The categories of this paper are: adap-org
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The categories of this paper are: stat
31
The categories of this paper are: ao-sci
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The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th