Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stratified Splitting for Efficient Monte Carlo Integration   The efficient evaluation of high-dimensional integrals is of importance in
both theoretical and practical fields of science, such as data science,
statistical physics, and machine learning. However, exact computation methods
suffer from the curse of dimensionality. However, due to the curse of
dimensionality, deterministic numerical methods are inefficient in
high-dimensional settings. Consequentially, for many practical problems, one
must resort to Monte Carlo estimation. In this paper, we introduce a novel
Sequential Monte Carlo technique called Stratified Splitting. The method
provides unbiased estimates and can handle various integrand types including
indicator functions, which are used in rare-event probability estimation
problems. Moreover, we demonstrate that a variant of the algorithm can achieve
polynomial complexity. The results of our numerical experiments suggest that
the Stratified Splitting method is capable of delivering accurate results for a
variety of integration problems.

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The categories of this paper are: math-ph
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The categories of this paper are: econ
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
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The categories of this paper are: eess
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The categories of this paper are: comp-gas
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The categories of this paper are: q-alg
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph