Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal scaling of random-walk Metropolis algorithms using Bayesian
  large-sample asymptotics   High-dimensional limit theorems have been shown useful to derive tuning rules
for finding the optimal scaling in random-walk Metropolis algorithms. The
assumptions under which weak convergence results are proved are however
restrictive: the target density is typically assumed to be of a product form.
Users may thus doubt the validity of such tuning rules in practical
applications. In this paper, we shed some light on optimal-scaling problems
from a different perspective, namely a large-sample one. This allows to prove
weak convergence results under realistic assumptions and to propose novel
parameter-dimension-dependent tuning guidelines. The proposed guidelines are
consistent with previous ones when the target density is close to having a
product form, and the results highlight that the correlation structure has to
be accounted for to avoid performance deterioration if that is not the case,
while justifying the use of a natural (asymptotically exact) approximation to
the correlation matrix that can be employed for the very first algorithm run.

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The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
1
The categories of this paper are: cs
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: solv-int