Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Use of Gaussian Processes in System Identification   Gaussian processes are used in machine learning to learn input-output
mappings from observed data. Gaussian process regression is based on imposing a
Gaussian process prior on the unknown regressor function and statistically
conditioning it on the observed data. In system identification, Gaussian
processes are used to form time series prediction models such as non-linear
finite-impulse response (NFIR) models as well as non-linear autoregressive
(NARX) models. Gaussian process state-space models (GPSS) can be used to learn
the dynamic and measurement models for a state-space representation of the
input-output data. Temporal and spatio-temporal Gaussian processes can be
directly used to form regressor on the data in the time domain. The aim of this
article is to briefly outline the main directions in system identification
methods using Gaussian processes.

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The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con