Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identifiability of Covariance Kernels in the Gaussian Process Regression
  Model   Gaussian process regression (GPR) model is a popular nonparametric regression
model. In GPR, features of the regression function such as varying degrees of
smoothness and periodicities are modeled through combining various covarinace
kernels, which are supposed to model certain effects. The covariance kernels
have unknown parameters which are estimated by the EM-algorithm or Markov Chain
Monte Carlo. The estimated parameters are keys to the inference of the features
of the regression functions, but identifiability of these parameters has not
been investigated. In this paper, we prove identifiability of covariance kernel
parameters in two radial basis mixed kernel GPR and radial basis and periodic
mixed kernel GPR. We also provide some examples about non-identifiable cases in
such mixed kernel GPRs.

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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: q-bio
3
The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: q-alg
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The categories of this paper are: adap-org
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph