Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pseudo-marginal Bayesian inference for supervised Gaussian process
  latent variable models   We introduce a Bayesian framework for inference with a supervised version of
the Gaussian process latent variable model. The framework overcomes the high
correlations between latent variables and hyperparameters by using an unbiased
pseudo estimate for the marginal likelihood that approximately integrates over
the latent variables. This is used to construct a Markov Chain to explore the
posterior of the hyperparameters. We demonstrate the procedure on simulated and
real examples, showing its ability to capture uncertainty and multimodality of
the hyperparameters and improved uncertainty quantification in predictions when
compared with variational inference.

10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math