An efficient forward-backward algorithm for an explicit-duration hidden Markov model
Abstract
Existing algorithms for estimating the model parameters of an explicit-duration hidden Markov model (HMM) usually require computations as large as O((MD/sup 2/ + M/sup 2/)T) or O(M/sup 2/ DT), where M is the number of states; D is the maximum possible interval between state transitions; and T is the period of observations used to estimate the model parameters. Because of such computational requirements, these algorithms are not practical when we wish to construct an HMM model with large state space and large explicit state duration and process a large amount of measurement data to obtain high accuracy. We propose a new forward-backward algorithm whose computational complexity is only O((MD + M/sup 2/)T), a reduction by almost a factor of D when D > M and whose memory requirement is O(MT). As an application example, we discuss an HMM characterization of access traffic observed at a large-scale Web site: we formulate the Web access pattern in terms of an HMM with explicit duration and estimate the model parameters using our algorithm.
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The most common choice for modeling the state duration is the multinomial due to its simplicity.
“The most common choice for modeling the state duration is the multinomial [18,21,34,43] due to its simplicity.”
From Efficient duration and hierarchical modeling for human activity recognition (2009) · cites this paper as [43]
By taking advantage of the determinism of m_t, the complexity of HSMM can be reduced to O(|Q|^2 M T), or even better to O((|Q|M+|Q|^2)T).
“By taking advantage of the determinism of m t (i.e.conditionally on a given state, m t+1 = m t -1), the complexity can be reduced to O (|Q | 2 M T ), or even better to O ((|Q |M +|Q | 2 )T )”
From Efficient duration and hierarchical modeling for human activity recognition (2009) · cites this paper as [43]
Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743 · see citing works
On the self-similar nature of Ethernet traffic (extended version)
Leland, W. E., Taqqu, M. S., Willinger, W., & Wilson, D. V. (1994) · IEEE/ACM Transactions on Networking · doi:10.1109/90.282603 · Cited by 5,232 · see citing works
Continuously variable duration hidden Markov models for automatic speech recognition
Levinson, S. E. (1986) · Computer Speech & Language · doi:10.1016/s0885-2308(86)80009-2 · Cited by 457 · see citing works
On the effect of traffic self-similarity on network performance
Park, K., Kim, G., & Crovella, M. E. (1997) · Cited by 154 · see citing works
Effect of traffic self-similarity on network performance
Park, K., Kim, G., & Crovella, M. E. (1997) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.290419 · Cited by 117 · see citing works
Modeling state durations in hidden Markov models for automatic speech recognition
Ramesh, P., & Wilpon, J. G. (1992) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1992.225892 · Cited by 76 · see citing works
Multiple time scale congestion control for self-similar network traffic
Tuan, T., & Park, K. (1999) · Performance Evaluation · doi:10.1016/s0166-5316(99)00024-3 · Cited by 70 · see citing works
On the complexity of explicit duration HMM's
Mitchell, C., Harper, M., & Jamieson, L. (1995) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.388149 · Cited by 63 · see citing works
A hidden semi-Markov model for web workload self-similarity
Yu, S., Liu, Z., Squillante, M. S., Xia, C. H., & Zhang, L. (2003) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/ipccc.2002.995137 · Cited by 29 · see citing works
Self-citationOn hidden fractal model signal processing
Krishnamurthy, V., Moore, J. B., & Chung, S. (1991) · Signal Processing · doi:10.1016/0165-1684(91)90130-b · Cited by 25 · see citing works
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Zen, H. (2007) · Cited by 8 · see citing works
Duong, T. V., Phung, D., Bui, H., & Venkatesh, S. (2005) · International Conference on Intelligent Sensors, Sensor Networks and Information Processing · doi:10.1109/issnip.2005.1595592 · Cited by 8 · see citing works
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Ntantamis, C. (2009) · SSRN Electronic Journal · doi:10.2139/ssrn.1343726 · Cited by 8 · see citing works
- Authors
- Yu, S.-Z., & Kobayashi, H.
- Affiliations
- Princeton University (US)
- Venue
- IEEE Signal Processing Letters
- Published
- 2003-01
- Volume
- 10
- Issue
- 1
- Pages
- 11-14
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W2153331007
- Cited by
- 226
- References
- 10
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 11 · 2023: 6 · 2022: 8 · 2021: 10 · 2020: 11 · 2019: 12 · 2018: 11 · 2017: 16 · 2016: 10 · 2015: 12 · 2014: 19 · 2013: 13 · 2012: 12
- FWCI
- 5.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Signal Processing Letters (publishedVersion)
- Funders
- George Mason University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- forward-backward algorithm, parameter estimation, computational complexity reduction, space complexity, web traffic modeling, state duration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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