Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Forecasting Future Murders of Mr. Boddy by Numerical Weather Prediction   Despite a previous description of his state as a stable fixed point, just
past midnight this morning Mr. Boddy was murdered again. In fact, over 70 years
Mr. Boddy has been reported murdered $10^6$ times, while there exist no
documented attempts at intervention. Using variational data assimilation, we
train a model of Mr. Boddy's dynamics on the time series of observed murders,
to forecast future murders. The parameters to be estimated include instrument,
location, and murderer. We find that a successful estimation requires three
additional elements. First, to minimize the effects of selection bias, generous
ranges are placed on parameter searches, permitting values such as the Cliff,
the Poisoned Apple, and the Wife. Second, motive, which was not considered
relevant to previous murders, is added as a parameter. Third, Mr. Boddy's
little-known asthmatic condition is considered as an alternative cause of
death. Following this morning's event, the next local murder is forecast for
17:19:03 EDT this afternoon, with a standard deviation of seven hours, at The
Kitchen at 4330 Katonah Avenue, Bronx, NY, 10470, with either the Lead Pipe or
the Lead Bust of Washington Irving. The motive is: Case of Mistaken Identity,
and there was no convergence upon a murderer. Testing of the procedure's
predictive power will involve catching the D train to 205th Street and a few
transfers over to Katonah Avenue, and sitting around waiting with our eyes
peeled. We discuss the problem of identifying a global solution - that is, the
best reason for murder on a landscape riddled with pretty-decent reasons. We
also discuss the procedure's assumption of Gaussian-distributed errors, which
will under-predict rare events. This under-representation of highly improbable
events may be offset by the fact that the training data, after all, consists of
multiple murders of a single person.

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The categories of this paper are: q-bio
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: stat
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The categories of this paper are: supr-con
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The categories of this paper are: cond-mat
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The categories of this paper are: bayes-an
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The categories of this paper are: plasm-ph
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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: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: econ
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph