Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hidden Markov Models with mixtures as emission distributions   In unsupervised classification, Hidden Markov Models (HMM) are used to
account for a neighborhood structure between observations. The emission
distributions are often supposed to belong to some parametric family. In this
paper, a semiparametric modeling where the emission distributions are a mixture
of parametric distributions is proposed to get a higher flexibility. We show
that the classical EM algorithm can be adapted to infer the model parameters.
For the initialisation step, starting from a large number of components, a
hierarchical method to combine them into the hidden states is proposed. Three
likelihood-based criteria to select the components to be combined are
discussed. To estimate the number of hidden states, BIC-like criteria are
derived. A simulation study is carried out both to determine the best
combination between the merging criteria and the model selection criteria and
to evaluate the accuracy of classification. The proposed method is also
illustrated using a biological dataset from the model plant Arabidopsis
thaliana. A R package HMMmix is freely available on the CRAN.

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The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: plasm-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
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The categories of this paper are: q-fin