Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recovering the state sequence of hidden Markov models using mean-field
  approximations   Inferring the sequence of states from observations is one of the most
fundamental problems in Hidden Markov Models. In statistical physics language,
this problem is equivalent to computing the marginals of a one-dimensional
model with a random external field. While this task can be accomplished through
transfer matrix methods, it becomes quickly intractable when the underlying
state space is large.
  This paper develops several low-complexity approximate algorithms to address
this inference problem when the state space becomes large. The new algorithms
are based on various mean-field approximations of the transfer matrix. Their
performances are studied in detail on a simple realistic model for DNA
pyrosequencing.

15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat