Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The MCMC split sampler: A block Gibbs sampling scheme for latent
  Gaussian models   A novel computationally efficient Markov chain Monte Carlo (MCMC) scheme for
latent Gaussian models (LGMs) is proposed in this paper. The sampling scheme is
a two block Gibbs sampling scheme designed to exploit the model structure of
LGMs. We refer to the proposed sampling scheme as the MCMC split sampler. The
principle idea behind the MCMC split sampler is to split the latent Gaussian
parameters into two vectors. The former vector consists of latent parameters
which appear in the data density function, while the latter vector consists of
latent parameters which do not appear in it. The former vector is placed in the
first block of the proposed sampling scheme and the latter vector is placed in
the second block along with any potential hyperparameters. The resulting
conditional posterior density functions within the blocks allow the MCMC split
sampler to handle, by design, LGMs with latent models imposed on more than just
the mean structure of the data density function. The MCMC split sampler is also
designed to be applicable for any choice of a parametric data density function.
Moreover, it scales well in terms of computational efficiency when the
dimension of the latent model increase.

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The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: physics
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The categories of this paper are: math