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Journal articles on the topic 'Hidden Markov Models'

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Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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1

Grewal, Jasleen K., Martin Krzywinski, and Naomi Altman. "Markov models — hidden Markov models." Nature Methods 16, no. 9 (August 30, 2019): 795–96. http://dx.doi.org/10.1038/s41592-019-0532-6.

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2

Eddy, Sean R. "Hidden Markov models." Current Opinion in Structural Biology 6, no. 3 (June 1996): 361–65. http://dx.doi.org/10.1016/s0959-440x(96)80056-x.

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3

Adams, Stephen, Peter A. Beling, and Randy Cogill. "Feature Selection for Hidden Markov Models and Hidden Semi-Markov Models." IEEE Access 4 (2016): 1642–57. http://dx.doi.org/10.1109/access.2016.2552478.

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4

Kersting, K., L. De Raedt, and T. Raiko. "Logical Hidden Markov Models." Journal of Artificial Intelligence Research 25 (April 19, 2006): 425–56. http://dx.doi.org/10.1613/jair.1675.

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Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov models to deal with sequences of structured symbols in the form of logical atoms, rather than flat characters. This note formally introduces LOHMMs and presents solutions to the three central inference problems for LOHMMs: evaluation, most likely hidden state sequence and parameter estimation. The resulting representation and algorithms are experimentally evaluated on problems from the domain of bioinformatics.
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5

Forchhammer, S., and J. Rissanen. "Partially hidden Markov models." IEEE Transactions on Information Theory 42, no. 4 (July 1996): 1253–56. http://dx.doi.org/10.1109/18.508852.

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6

Altman, Rachel MacKay. "Mixed Hidden Markov Models." Journal of the American Statistical Association 102, no. 477 (March 2007): 201–10. http://dx.doi.org/10.1198/016214506000001086.

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7

Barrett, Christian, Richard Hughey, and Kevin Karplus. "Scoring hidden Markov models." Bioinformatics 13, no. 2 (1997): 191–99. http://dx.doi.org/10.1093/bioinformatics/13.2.191.

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8

Eddy, S. R. "Profile hidden Markov models." Bioinformatics 14, no. 9 (October 1, 1998): 755–63. http://dx.doi.org/10.1093/bioinformatics/14.9.755.

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9

Dannemann, Jörn. "Semiparametric Hidden Markov Models." Journal of Computational and Graphical Statistics 21, no. 3 (July 2012): 677–92. http://dx.doi.org/10.1080/10618600.2012.681264.

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10

Farcomeni, Alessio. "Hidden Markov partition models." Statistics & Probability Letters 81, no. 12 (December 2011): 1766–70. http://dx.doi.org/10.1016/j.spl.2011.07.012.

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11

Bueno, Marcos L. P., Arjen Hommersom, Peter J. F. Lucas, and Alexis Linard. "Asymmetric hidden Markov models." International Journal of Approximate Reasoning 88 (September 2017): 169–91. http://dx.doi.org/10.1016/j.ijar.2017.05.011.

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12

Yu, Shun-Zheng. "Hidden semi-Markov models." Artificial Intelligence 174, no. 2 (February 2010): 215–43. http://dx.doi.org/10.1016/j.artint.2009.11.011.

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13

Souissi, Abdessatar, and El Gheteb Soueidi. "Entangled Hidden Markov Models." Chaos, Solitons & Fractals 174 (September 2023): 113804. http://dx.doi.org/10.1016/j.chaos.2023.113804.

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14

Grewal, Jasleen K., Martin Krzywinski, and Naomi Altman. "Markov models — training and evaluation of hidden Markov models." Nature Methods 17, no. 2 (February 2020): 121–22. http://dx.doi.org/10.1038/s41592-019-0702-6.

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15

Mitrophanov, Alexander Yu, Alexandre Lomsadze, and Mark Borodovsky. "Sensitivity of hidden Markov models." Journal of Applied Probability 42, no. 3 (September 2005): 632–42. http://dx.doi.org/10.1239/jap/1127322017.

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We derive a tight perturbation bound for hidden Markov models. Using this bound, we show that, in many cases, the distribution of a hidden Markov model is considerably more sensitive to perturbations in the emission probabilities than to perturbations in the transition probability matrix and the initial distribution of the underlying Markov chain. Our approach can also be used to assess the sensitivity of other stochastic models, such as mixture processes and semi-Markov processes.
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16

Mitrophanov, Alexander Yu, Alexandre Lomsadze, and Mark Borodovsky. "Sensitivity of hidden Markov models." Journal of Applied Probability 42, no. 03 (September 2005): 632–42. http://dx.doi.org/10.1017/s002190020000067x.

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We derive a tight perturbation bound for hidden Markov models. Using this bound, we show that, in many cases, the distribution of a hidden Markov model is considerably more sensitive to perturbations in the emission probabilities than to perturbations in the transition probability matrix and the initial distribution of the underlying Markov chain. Our approach can also be used to assess the sensitivity of other stochastic models, such as mixture processes and semi-Markov processes.
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17

SETIAWATY, B. "IDENTIFIABILITY OF HIDDEN MARKOV MODELS." Journal of Mathematics and Its Applications 4, no. 1 (July 1, 2005): 13. http://dx.doi.org/10.29244/jmap.4.1.13-22.

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18

De Fonzo, Valeria, Filippo Aluffi-Pentini, and Valerio Parisi. "Hidden Markov Models in Bioinformatics." Current Bioinformatics 2, no. 1 (January 1, 2007): 49–61. http://dx.doi.org/10.2174/157489307779314348.

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19

Sisson, Scott. "Hidden Markov Models for Bioinformatics." Journal of the Royal Statistical Society: Series A (Statistics in Society) 167, no. 1 (February 2004): 194–95. http://dx.doi.org/10.1111/j.1467-985x.2004.298_13.x.

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20

Nagaraja, Haikady N. "Inference in Hidden Markov Models." Technometrics 48, no. 4 (November 2006): 574–75. http://dx.doi.org/10.1198/tech.2006.s440.

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21

Boufounos, Petros, Sameh El-Difrawy, and Dan Ehrlich. "Basecalling using hidden Markov models." Journal of the Franklin Institute 341, no. 1-2 (January 2004): 23–36. http://dx.doi.org/10.1016/j.jfranklin.2003.12.008.

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22

Di Masi, G. B., and Ł. Stettner. "Ergodicity of hidden Markov models." Mathematics of Control, Signals, and Systems 17, no. 4 (June 29, 2005): 269–96. http://dx.doi.org/10.1007/s00498-005-0153-8.

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23

Nam Soo Kim and Dong Kook Kim. "Filtering on hidden Markov models." IEEE Signal Processing Letters 7, no. 9 (September 2000): 253–55. http://dx.doi.org/10.1109/97.863148.

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24

Sanches, I. "Noise-compensated hidden Markov models." IEEE Transactions on Speech and Audio Processing 8, no. 5 (2000): 533–40. http://dx.doi.org/10.1109/89.861372.

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25

Saon, G., and Jen-Tzung Chien. "Bayesian Sensing Hidden Markov Models." IEEE Transactions on Audio, Speech, and Language Processing 20, no. 1 (January 2012): 43–54. http://dx.doi.org/10.1109/tasl.2011.2129911.

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26

Meloni, Lu�s Geraldo P. "Learning discrete hidden Markov models." Computer Applications in Engineering Education 8, no. 3-4 (2000): 141–49. http://dx.doi.org/10.1002/1099-0542(2000)8:3/4<141::aid-cae2>3.0.co;2-y.

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27

Kabayashi, Kingo, Shun-Ichi Amari, and Hisashi Ito. "Equivalence of hidden Markov models." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 74, no. 7 (1991): 85–94. http://dx.doi.org/10.1002/ecjc.4430740709.

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28

Kordnoori, Shirin, Hamidreza Mostafaei, Shaghayegh Kordnoori, and Mohammad Mohsen Ostadrahimi. "Evaluating the CDMA System Using Hidden Markov and Semi Hidden Markov Models." IPTEK The Journal for Technology and Science 31, no. 3 (January 28, 2021): 295. http://dx.doi.org/10.12962/j20882033.v31i3.7016.

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29

SETIAWATY, B. "EQUIVALENT REPRESENTATIONS OF HIDDEN MARKOV MODELS." Journal of Mathematics and Its Applications 2, no. 1 (July 1, 2003): 1. http://dx.doi.org/10.29244/jmap.2.1.1-12.

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In this article, we classify the class of hidden Markov models through the laws of the observation processes, since the Markov chains are not observable. Here, we also present some properties regarding this classification.
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30

Visser, Ingmar, Maartje E. J. Raijmakers, and Peter C. M. Molenaar. "Fitting Hidden Markov Models to Psychological Data." Scientific Programming 10, no. 3 (2002): 185–99. http://dx.doi.org/10.1155/2002/874560.

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Markov models have been used extensively in psychology of learning. Applications of hidden Markov models are rare however. This is partially due to the fact that comprehensive statistics for model selection and model assessment are lacking in the psychological literature. We present model selection and model assessment statistics that are particularly useful in applying hidden Markov models in psychology. These statistics are presented and evaluated by simulation studies for a toy example. We compare AIC, BIC and related criteria and introduce a prediction error measure for assessing goodness-of-fit. In a simulation study, two methods of fitting equality constraints are compared. In two illustrative examples with experimental data we apply selection criteria, fit models with constraints and assess goodness-of-fit. First, data from a concept identification task is analyzed. Hidden Markov models provide a flexible approach to analyzing such data when compared to other modeling methods. Second, a novel application of hidden Markov models in implicit learning is presented. Hidden Markov models are used in this context to quantify knowledge that subjects express in an implicit learning task. This method of analyzing implicit learning data provides a comprehensive approach for addressing important theoretical issues in the field.
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31

GHAHRAMANI, ZOUBIN. "AN INTRODUCTION TO HIDDEN MARKOV MODELS AND BAYESIAN NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 01 (February 2001): 9–42. http://dx.doi.org/10.1142/s0218001401000836.

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We provide a tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks. This perspective makes it possible to consider novel generalizations of hidden Markov models with multiple hidden state variables, multiscale representations, and mixed discrete and continuous variables. Although exact inference in these generalizations is usually intractable, one can use approximate inference algorithms such as Markov chain sampling and variational methods. We describe how such methods are applied to these generalized hidden Markov models. We conclude this review with a discussion of Bayesian methods for model selection in generalized HMMs.
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32

Burgarth, Daniel. "Identifying combinatorially symmetric Hidden Markov Models." Electronic Journal of Linear Algebra 34 (February 21, 2018): 393–98. http://dx.doi.org/10.13001/1081-3810.3651.

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A sufficient criterion for the unique parameter identification of combinatorially symmetric Hidden Markov Models, based on the structure of their transition matrix, is provided. If the observed states of the chain form a zero forcing set of the graph of the Markov model, then it is uniquely identifiable and an explicit reconstruction method is given.
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33

NAKAMURA, Atsushi. "Speech Recognition using Hidden Markov Models." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 6 (1998): 1084–90. http://dx.doi.org/10.3156/jfuzzy.10.6_1084.

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34

BALDI, PIERRE. "Substitution Matrices and Hidden Markov Models." Journal of Computational Biology 2, no. 3 (January 1995): 487–91. http://dx.doi.org/10.1089/cmb.1995.2.487.

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35

Merhav, N. "Universal classification for hidden Markov models." IEEE Transactions on Information Theory 37, no. 6 (1991): 1586–94. http://dx.doi.org/10.1109/18.104319.

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36

ANIGBOGU, J. C., and A. BELAÏD. "HIDDEN MARKOV MODELS IN TEXT RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 06 (December 1995): 925–58. http://dx.doi.org/10.1142/s0218001495000389.

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A multi-level multifont character recognition is presented. The system proceeds by first delimiting the context of the characters. As a way of enhancing system performance, typographical information is extracted and used for font identification before actual character recognition is performed. This has the advantage of sure character identification as well as text reproduction in its original form. The font identification is based on decision trees where the characters are automatically arranged differently in confusion classes according to the physical characteristics of fonts. The character recognizers are built around the first and second order hidden Markov models (HMM) as well as Euclidean distance measures. The HMMs use the Viterbi and the Extended Viterbi algorithms to which enhancements were made. Also present is a majority-vote system that polls the other systems for “advice” before deciding on the identity of a character. Among other things, this last system is shown to give better results than each of the other systems applied individually. The system finally uses combinations of stochastic and dictionary verification methods for word recognition and error-correction.
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37

Resnick, Sidney, and Ajay Subramanian. "Heavy tailed hidden semi-markov models." Communications in Statistics. Stochastic Models 14, no. 1-2 (January 1998): 319–34. http://dx.doi.org/10.1080/15326349808807474.

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38

Pak, Charles, and James Cannady. "Risk forecast using hidden Markov models." ACM SIGITE Research in IT 7, no. 2 (July 2010): 4–15. http://dx.doi.org/10.1145/1841325.1841326.

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39

Vobbilisetty, Rohit, Fabio Di Troia, Richard M. Low, Corrado Aaron Visaggio, and Mark Stamp. "Classic cryptanalysis using hidden Markov models." Cryptologia 41, no. 1 (March 16, 2016): 1–28. http://dx.doi.org/10.1080/01611194.2015.1126660.

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40

Green, Peter J., and Sylvia Richardson. "Hidden Markov Models and Disease Mapping." Journal of the American Statistical Association 97, no. 460 (December 2002): 1055–70. http://dx.doi.org/10.1198/016214502388618870.

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41

Scott, Steven L. "Bayesian Methods for Hidden Markov Models." Journal of the American Statistical Association 97, no. 457 (March 2002): 337–51. http://dx.doi.org/10.1198/016214502753479464.

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42

Scott, Steven L., Gareth M. James, and Catherine A. Sugar. "Hidden Markov Models for Longitudinal Comparisons." Journal of the American Statistical Association 100, no. 470 (June 2005): 359–69. http://dx.doi.org/10.1198/016214504000001592.

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43

Li, Qian, Cui Yang, Jian-Qi Zhang, and Dong-Yang Zhang. "Hidden Markov Models for background clutter." Optical Engineering 52, no. 7 (July 16, 2013): 073108. http://dx.doi.org/10.1117/1.oe.52.7.073108.

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44

Vlontzos, J. A., and S. Y. Kung. "Hidden Markov models for character recognition." IEEE Transactions on Image Processing 1, no. 4 (1992): 539–43. http://dx.doi.org/10.1109/83.199925.

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45

White, L. B. "Cartesian hidden Markov models with applications." IEEE Transactions on Signal Processing 40, no. 6 (June 1992): 1601–4. http://dx.doi.org/10.1109/78.139272.

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46

Kwong, S., Q. H. He, and K. F. Man. "Training approach for hidden Markov models." Electronics Letters 32, no. 17 (1996): 1554. http://dx.doi.org/10.1049/el:19961080.

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47

Bechhoefer, John. "Hidden Markov models for stochastic thermodynamics." New Journal of Physics 17, no. 7 (July 3, 2015): 075003. http://dx.doi.org/10.1088/1367-2630/17/7/075003.

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48

Han, Guangyue. "Limit Theorems in Hidden Markov Models." IEEE Transactions on Information Theory 59, no. 3 (March 2013): 1311–28. http://dx.doi.org/10.1109/tit.2012.2226701.

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49

De Maio, Nicola, Ian Holmes, Christian Schlötterer, and Carolin Kosiol. "Estimating Empirical Codon Hidden Markov Models." Molecular Biology and Evolution 30, no. 3 (November 27, 2012): 725–36. http://dx.doi.org/10.1093/molbev/mss266.

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50

Panerati, Jacopo, Giovanni Beltrame, Nicolas Schwind, Stefan Zeltner, and Katsumi Inoue. "Probabilistic Resilience in Hidden Markov Models." IOP Conference Series: Materials Science and Engineering 131 (May 2016): 012007. http://dx.doi.org/10.1088/1757-899x/131/1/012007.

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