POINTS OF SIGNIFICANCE

Markov models — training and evaluation of hidden Markov models

“With one eye you are looking at the outside world, while with the other you are looking within yourself.” —Amedeo Modigliani

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Modeling and parameter estimation of a Hidden Markov model (HMM) for an unstable coin.
Fig. 2: The calculation of the forward and backward probabilities for the training sequence HHTTT using initial estimates.
Fig. 3: Accuracy of HMM parameter estimates improves with larger training sets.

References

  1. 1.

    Grewal, J., Krzywinski, M. & Altman, N. Nat. Methods 16, 795–796 (2019).

  2. 2.

    Rabiner, L. R. Proc. IEEE 77, 257–286 (1989).

  3. 3.

    Durbin, R., Eddy, S., Krogh, A. & Mitchison, G. (1996). Biological Sequence Analysis (Cambridge Univ. Press, 1996).

  4. 4.

    Rabiner, L. R. & Juang, B. H. IEEE ASSP Mag. 3, 4–16 (1986).

Download references

Author information

Correspondence to Martin Krzywinski.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Grewal, J.K., Krzywinski, M. & Altman, N. Markov models — training and evaluation of hidden Markov models. Nat Methods 17, 121–122 (2020). https://doi.org/10.1038/s41592-019-0702-6

Download citation