A Bayesian model selection criterion for HMM topology optimization

@article{Biem2002ABM,
  title={A Bayesian model selection criterion for HMM topology optimization},
  author={Alain Biem and Jin-Young Ha and Jayashree Subrahmonia},
  journal={2002 IEEE International Conference on Acoustics, Speech, and Signal Processing},
  year={2002},
  volume={1},
  pages={I-989-I-992}
}
This paper addresses the problem of estimating the optimal Hidden Markov Model (HMM) topology. The optimal topology is defined as the one that gives the smallest error-rate with the minimal number of parameters. The paper introduces a Bayesian model selection criterion that is suitable for Continuous Hidden Markov Models topology optimization. The criterion is derived from the Laplacian approximation of the posterior of a model structure, and shares the algorithmic simplicity of conventional… CONTINUE READING
Highly Cited
This paper has 21 citations. REVIEW CITATIONS
14 Citations
8 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 14 extracted citations

References

Publications referenced by this paper.
Showing 1-8 of 8 references

Bayes factors

  • R. E. Kass, A. E Raftery
  • Technical Report 254, University of Washington…
  • 1994
Highly Influential
3 Excerpts

MML and Bayesianism: similarities and differences (Introduction to Minimum Encoding Inference· part ii:

  • J. Olivier, R. Baxter
  • Technical Report 206,
  • 1994

Bayesian Interpolation

  • D. MacKay
  • Neural Compu� tation, vol. 4, no. 3, pp. 415-447…
  • 1992
1 Excerpt

EDF statistics for goodness of fit and some comparisons

  • M. A. Stephens
  • Journal of the American Statistical Association…
  • 1974
1 Excerpt

Similar Papers

Loading similar papers…