Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Finite-dimensional approximation of Gaussian processes with inequality
  constraints   Due to their flexibility, Gaussian processes (GPs) have been widely used in
nonparametric function estimation. A prior information about the underlying
function is often available. For instance, the physical system (computer model
output) may be known to satisfy inequality constraints with respect to some or
all inputs. We develop a finite-dimensional approximation of GPs capable of
incorporating inequality constraints and noisy observations for computer model
emulators. It is based on a linear combination between Gaussian random
coefficients and deterministic basis functions. By this methodology , the
inequality constraints are respected in the entire domain. The mean and the
maximum of the posterior distribution are well defined. A simulation study to
show the efficiency and the performance of the proposed model in term of
predictive accuracy and uncertainty quantification is included.

7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg