Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tutorial for Bayesian forensic likelihood ratio   In the Bayesian paradigm for presenting forensic evidence to court, it is
recommended that the weight of the evidence be summarized as a likelihood ratio
(LR) between two opposing hypotheses of how the evidence could have been
produced. Such LRs are necessarily based on probabilistic models, the
parameters of which may be uncertain. It has been suggested by some authors
that the value of the LR, being a function of the model parameters should
therefore also be considered uncertain and that this uncertainty should be
communicated to the court. In this tutorial, we consider a simple example of a
'fully Bayesian' solution, where model uncertainty is integrated out to produce
a value for the LR which is not uncertain. We show that this solution agrees
with common sense. In particular, the LR magnitude is a function of the amount
of data that is available to estimate the model parameters.

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The categories of this paper are: dg-ga
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The categories of this paper are: nlin
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The categories of this paper are: q-alg
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The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: q-bio
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The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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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: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ph