Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variable transformation to obtain geometric ergodicity in the
  random-walk Metropolis algorithm   A random-walk Metropolis sampler is geometrically ergodic if its equilibrium
density is super-exponentially light and satisfies a curvature condition
[Stochastic Process. Appl. 85 (2000) 341-361]. Many applications, including
Bayesian analysis with conjugate priors of logistic and Poisson regression and
of log-linear models for categorical data result in posterior distributions
that are not super-exponentially light. We show how to apply the
change-of-variable formula for diffeomorphisms to obtain new densities that do
satisfy the conditions for geometric ergodicity. Sampling the new variable and
mapping the results back to the old gives a geometrically ergodic sampler for
the original variable. This method of obtaining geometric ergodicity has very
wide applicability.

27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
1
The categories of this paper are: cs
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio