Distributions associated with general runs and patterns in hidden Markov models

Details Distributions associated with general runs and patterns in hidden Markov models Distributions associated with general runs and patterns in hidden Markov models

2007-06-27

arxiv.org/abs/0706.3985v2

Annals of Applied Statistics 2007, Vol. 1, No. 2, 585-611

Statistics

Methodology

Published in at http://dx.doi.org/10.1214/07-AOAS125 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/07-AOAS125

This paper gives a method for computing distributions associated with patterns in the state sequence of a hidden Markov model, conditional on observing all or part of the observation sequence. Probabilities are computed for very general classes of patterns (competing patterns and generalized later patterns), and thus, the theory includes as special cases results for a large class of problems that have wide application. The unobserved state sequence is assumed to be Markovian with a general order of dependence. An auxiliary Markov chain is associated with the state sequence and is used to simplify the computations. Two examples are given to illustrate the use of the methodology. Whereas the first application is more to illustrate the basic steps in applying the theory, the second is a more detailed application to DNA sequences, and shows that the methods can be adapted to include restrictions related to biological knowledge.

Affiliated with

Also associated with

No associations

Rating

Distributions associated with general runs and patterns in hidden Markov models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Distributions associated with general runs and patterns in hidden Markov models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Distributions associated with general runs and patterns in hidden Markov models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-255770