Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gaussian process modelling of multiple short time series   We present techniques for effective Gaussian process (GP) modelling of
multiple short time series. These problems are common when applying GP models
independently to each gene in a gene expression time series data set. Such sets
typically contain very few time points. Naive application of common GP
modelling techniques can lead to severe over-fitting or under-fitting in a
significant fraction of the fitted models, depending on the details of the data
set. We propose avoiding over-fitting by constraining the GP length-scale to
values that focus most of the energy spectrum to frequencies below the Nyquist
frequency corresponding to the sampling frequency in the data set.
Under-fitting can be avoided by more informative priors on observation noise.
Combining these methods allows applying GP methods reliably automatically to
large numbers of independent instances of short time series. This is
illustrated with experiments with both synthetic data and real gene expression
data.

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The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th