Classroom note: Building simple hidden Markov models

Abstract

Hidden Markov models (HMMs) are widely used in bioinformatics, speech recognition and many other areas. This note presents HMMs via the framework of classical Markov chain models. A simple example is given to illustrate the model. An estimation method for the transition probabilities of the hidden states is also discussed.

Acknowledgement

The authors would like to thank the anonymous referees for their helpful comments, suggestions and corrections in revising the paper.

Notes

† The author to whom correspondence should be addressed.

* Research supported in part by RGC Grant No. HKU 7126/02P and HKU CRCG Grant Nos. 10203907, 10204437, 10203501, 10205105 and 10204436.

* Research supported in part by RGC Grant No. HKU 7126/02P and HKU CRCG Grant Nos. 10203907, 10204437, 10203501, 10205105 and 10204436.

Additional information

Notes on contributors

Michael K. Ng

† The author to whom correspondence should be addressed. * Research supported in part by RGC Grant No. HKU 7126/02P and HKU CRCG Grant Nos. 10203907, 10204437, 10203501, 10205105 and 10204436.