Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficiency and computability of MCMC with Langevin, Hamiltonian, and
  other matrix-splitting proposals   We analyse computational efficiency of Metropolis-Hastings algorithms with
AR(1) process proposals. These proposals include, as a subclass, discretized
Langevin diffusion (e.g. MALA) and discretized Hamiltonian dynamics (e.g. HMC).
  By including the effect of Metropolis-Hastings we extend earlier work by Fox
and Parker, who used matrix splitting techniques to analyse the performance and
improve efficiency of AR(1) processes for targeting Gaussian distributions.
  Our research enables analysis of MCMC methods that draw samples from
non-Gaussian target distributions by using AR(1) process proposals in
Metropolis-Hastings algorithms, by analysing the matrix splitting of the
precision matrix for a local Gaussian approximation of the non-Gaussian target.

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The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
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The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph