Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fundamentals of Gaussian CM Sequences   Markov processes are widely used in modeling random phenomena/problems.
However, they may not be adequate in some cases where more general processes
are needed. The conditionally Markov (CM) process is a generalization of the
Markov process based on conditioning. There are several classes of CM processes
(one of them is the class of reciprocal processes), which provide more
capability (than Markov) for modeling random phenomena. Reciprocal processes
have been used in many different applications (e.g., image processing, intent
inference, intelligent systems). In this paper, nonsingular Gaussian (NG) CM
sequences are studied, characterized, and their dynamic models are presented.
The presented results provide effective tools for studying reciprocal sequences
from the CM viewpoint, which is different from that of the literature. Also,
the presented models and characterizations serve as a basis for application of
CM sequences, e.g., in motion trajectory modeling with destination information.

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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: acc-phys
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The categories of this paper are: q-bio
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The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: chem-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-th
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The categories of this paper are: quant-ph