Bayesian Model Selection for Markov, Hidden Markov,
and Multinomial Models.
Dirac Research AB
, Signals and Systems, Uppsala University.
IEEE Signal Processing Letters,
Volume 14, No. 2, February 2007, Pages 129-132.
Model selection based on observed data sequences is used
to decide between different model structures within the
class of multinomial, Markov, and hidden Markov models.
In a unified Bayesian treatment, we derive posterior
probabilities for different model structures without
assuming prior knowledge of transition probabilities.
We emphasize the following tests:
- Given a particular data sequence of outcomes,
is each state equally likely?
- Do the data support an independent model,
or is a Markov model a more plausible description?
- Are two data sequences generated from
a) the same Markov model?
b) the same hidden Markov model?
For Markov models and independent multinomial models,
all results are exact. For hidden Markov models, the
exact solution is computationally prohibitive,
and instead, an approximate solution is proposed.
by Mathias Johansson.
entry in list of publications
This material is posted here with permission of the IEEE.
Such permission of the IEEE does not in any
way imply IEEE endorsement of any of Uppsala University's
products or services.
Internal or personal use of this material is permitted.
However, permission to reprint/republish this material for
advertising or promotional purposes or for creating new collective
works for resale or redistribution must be obtained
from the IEEE by writing to firstname.lastname@example.org.
By choosing to view this document, you agree to all
provisions of the copyright laws protecting it.