Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive ABC model choice and geometric summary statistics for hidden
  Gibbs random fields   Selecting between different dependency structures of hidden Markov random
field can be very challenging, due to the intractable normalizing constant in
the likelihood. We answer this question with approximate Bayesian computation
(ABC) which provides a model choice method in the Bayesian paradigm. This comes
after the work of Grelaud et al. (2009) who exhibited sufficient statistics on
directly observed Gibbs random fields. But when the random field is latent, the
sufficiency falls and we complement the set with geometric summary statistics.
The general approach to construct these intuitive statistics relies on a
clustering analysis of the sites based on the observed colors and plausible
latent graphs. The efficiency of ABC model choice based on these statistics is
evaluated via a local error rate which may be of independent interest. As a
byproduct we derived an ABC algorithm that adapts the dimension of the summary
statistics to the dataset without distorting the model selection.

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The categories of this paper are: cond-mat
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The categories of this paper are: cs
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: ao-sci