Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Probability Based Independence Sampler for Bayesian Quantitative
  Learning in Graphical Log-Linear Marginal Models   Bayesian methods for graphical log-linear marginal models have not been
developed in the same extent as traditional frequentist approaches. In this
work, we introduce a novel Bayesian approach for quantitative learning for such
models. These models belong to curved exponential families that are difficult
to handle from a Bayesian perspective. Furthermore, the likelihood cannot be
analytically expressed as a function of the marginal log-linear interactions,
but only in terms of cell counts or probabilities.
  Posterior distributions cannot be directly obtained, and MCMC methods are
needed. Finally, a well-defined model requires parameter values that lead to
compatible marginal probabilities. Hence, any MCMC should account for this
important restriction. We construct a fully automatic and efficient MCMC
strategy for quantitative learning for graphical log-linear marginal models
that handles these problems. While the prior is expressed in terms of the
marginal log-linear interactions, we build an MCMC algorithm that employs a
proposal on the probability parameter space. The corresponding proposal on the
marginal log-linear interactions is obtained via parameter transformation.
  By this strategy, we achieve to move within the desired target space. At each
step, we directly work with well-defined probability distributions.
  Moreover, we can exploit a conditional conjugate setup to build an efficient
proposal on probability parameters. The proposed methodology is illustrated by
a simulation study and a real dataset.

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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int