Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On optimal direction gibbs sampling   Generalized Gibbs kernels are those that may take any direction not
necessarily bounded to each axis along the parameters of the objective
function. We study how to optimally choose such directions in a Directional,
random scan, Gibbs sampler setting. The optimal direction is chosen by
minimizing to the mutual information (Kullback-Leibler divergence) of two steps
of the MCMC for a truncated Normal objective function. The result is
generalized to be used when a Multivariate Normal (local) approximation is
available for the objective function. Three Gibbs direction distributions are
tested in highly skewed non-normal objective functions.

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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
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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: atom-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: solv-int
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The categories of this paper are: ao-sci