Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable random number generation for truncated log-concave
  distributions   Inverse transform sampling is an exceptionally general method to generate
non-uniform-distributed random numbers, but can be rather unstable when
simulating extremely truncated distributions. Many famous probability models
share a property called log-concavity, which is not affected by truncation, so
they can all be simulated via rejection sampling using Devroye's approach. This
sampler is based on rejection and thus more stable than inverse transform, and
uses a very simple envelope whose acceptance rate is guaranteed to be at least
20\%. The aim of this paper is threefold: firstly, to warn against the risk of
wrongly simulating from truncated distributions; secondly, to motivate a more
extensive use of rejection sampling to mitigate the issues; lastly, to motivate
Devroye's automatic method as a practical standard in the case of log-concave
distributions. We illustrate the proposal by means of simulations based on some
Tweedie distributions, for their relevance in regression analysis.

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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: econ
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The categories of this paper are: patt-sol
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The categories of this paper are: math
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The categories of this paper are: ao-sci
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The categories of this paper are: atom-ph
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The categories of this paper are: physics
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The categories of this paper are: mtrl-th