Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Infinite Mixture Model of Markov Chains   We propose a Bayesian nonparametric mixture model for prediction- and
information extraction tasks with an efficient inference scheme. It models
categorical-valued time series that exhibit dynamics from multiple underlying
patterns (e.g. user behavior traces). We simplify the idea of capturing these
patterns by hierarchical hidden Markov models (HHMMs) - and extend the existing
approaches by the additional representation of structural information. Our
empirical results are based on both synthetic- and real world data. They
indicate that the results are easily interpretable, and that the model excels
at segmentation and prediction performance: it successfully identifies the
generating patterns and can be used for effective prediction of future
observations.

26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn