Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Nonseparable Gaussian Stochastic Process with Application to
  Methylation Level Interpolation   Gaussian stochastic process (GaSP) has been widely used as a prior over
functions due to its flexibility and tractability in modeling. However, the
computational cost in evaluating the likelihood is $O(n^3)$, where $n$ is the
number of observed points in the process, as it requires to invert the
covariance matrix. This bottleneck prevents GaSP being widely used in
large-scale data. We propose a general class of nonseparable GaSP models for
multiple functional observations with a fast and exact algorithm, in which the
computation is linear ($O(n)$) and exact, requiring no approximation to compute
the likelihood. We show that the commonly used linear regression and separable
models are special cases of the proposed nonseparable GaSP model. Through the
study of an epigenetic application, the proposed nonseparable GaSP model can
accurately predict the genome-wide DNA methylation levels and compares
favorably to alternative methods, such as linear regression, random forest and
localized Kriging method. The algorithm for fast computation is implemented in
the ${\tt FastGaSP}$ R package on CRAN.

24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph