Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Asymptotically Efficient Identification of Known-Sensor Hidden Markov
  Models   We consider estimating the transition probability matrix of a finite-state
finite-observation alphabet hidden Markov model with known observation
probabilities. The main contribution is a two-step algorithm; a method of
moments estimator (formulated as a convex optimization problem) followed by a
single iteration of a Newton-Raphson maximum likelihood estimator. The two-fold
contribution of this letter is, firstly, to theoretically show that the
proposed estimator is consistent and asymptotically efficient, and secondly, to
numerically show that the method is computationally less demanding than
conventional methods - in particular for large data sets.

25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin