Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convergence Rates for a Class of Estimators Based on Stein's Method   Gradient information on the sampling distribution can be used to reduce the
variance of Monte Carlo estimators via Stein's method. An important application
is that of estimating an expectation of a test function along the sample path
of a Markov chain, where gradient information enables convergence rate
improvement at the cost of a linear system which must be solved. The
contribution of this paper is to establish theoretical bounds on convergence
rates for a class of estimators based on Stein's method. Our analysis accounts
for (i) the degree of smoothness of the sampling distribution and test
function, (ii) the dimension of the state space, and (iii) the case of
non-independent samples arising from a Markov chain. These results provide
insight into the rapid convergence of gradient-based estimators observed for
low-dimensional problems, as well as clarifying a curse-of-dimension that
appears inherent to such methods.

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The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
1
The categories of this paper are: cs
28
The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
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The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph