Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian generalized linear model for over and under dispersed counts   Bayesian models that can handle both over and under dispersed counts are rare
in the literature, perhaps because full probability distributions for dispersed
counts are rather difficult to construct. This note takes a first look at
Bayesian Conway-Maxwell-Poisson generalized linear models that can handle both
over and under dispersion yet retain the parsimony and interpretability of
classical count regression models. The focus is on providing an explicit
demonstration of Bayesian regression inferences for dispersed counts via a
Metropolis-Hastings algorithm. We illustrate the approach on two data analysis
examples and demonstrate some favourable frequentist properties via a
simulation study.

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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: physics
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc