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Journal articles on the topic 'Hiden Markov model'

Author: Grafiati

Published: 1 July 2022

Last updated: 31 July 2025

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1

Buckby, Jodie, Ting Wang, Jiancang Zhuang, and Kazushige Obara. "Model Checking for Hidden Markov Models." Journal of Computational and Graphical Statistics 29, no. 4 (2020): 859–74. http://dx.doi.org/10.1080/10618600.2020.1743295.

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2

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

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3

Tumilaar, Kezia, Yohanes Langi, and Altien Rindengan. "Hidden Markov Model." d'CARTESIAN 4, no. 1 (2015): 86. http://dx.doi.org/10.35799/dc.4.1.2015.8104.

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Hidden Markov Models (HMM) is a stochastic model and is essentially an extension of Markov Chain. In Hidden Markov Model (HMM) there are two types states: the observable states and the hidden states. The purpose of this research are to understand how hidden Markov model (HMM) and to understand how the solution of three basic problems on Hidden Markov Model (HMM) which consist of evaluation problem, decoding problem and learning problem. The result of the research is hidden Markov model can be defined as . The evaluation problem or to compute probability of the observation sequence given the mo

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4

Kim, Sae-Joong, Young-Han Jung, and Chong-Kwan Heo. "Analysis sports using the Hidden Markov Model." Korean Journal of Sports Science 26, no. 3 (2017): 1301–9. http://dx.doi.org/10.35159/kjss.2017.06.26.3.1301.

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5

Lay, Khin Khin, and Aung Cho. "Myanmar Named Entity Recognition with Hidden Markov Model." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (2019): 1144–47. http://dx.doi.org/10.31142/ijtsrd24012.

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6

Johansson, Mathias, and Tomas Olofsson. "Bayesian Model Selection for Markov, Hidden Markov, and Multinomial Models." IEEE Signal Processing Letters 14, no. 2 (2007): 129–32. http://dx.doi.org/10.1109/lsp.2006.882094.

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7

Bhatia, Varsha. "Applications of Hidden Markov Model in Wireless Sensor Network." International Journal of Psychosocial Rehabilitation 24, no. 4 (2020): 6549–57. http://dx.doi.org/10.37200/ijpr/v24i4/pr2020465.

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8

Ghods, Vahid, and Mohammad Karim Sohrabi. "Online Farsi Handwritten Character Recognition Using Hidden Markov Model." Journal of Computers 11, no. 2 (2016): 169–75. http://dx.doi.org/10.17706/jcp.11.2.169-175.

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9

Ye, Fei, and Yifei Wang. "A Novel Method for Decoding Any High-Order Hidden Markov Model." Discrete Dynamics in Nature and Society 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/231704.

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This paper proposes a novel method for decoding any high-order hidden Markov model. First, the high-order hidden Markov model is transformed into an equivalent first-order hidden Markov model by Hadar’s transformation. Next, the optimal state sequence of the equivalent first-order hidden Markov model is recognized by the existing Viterbi algorithm of the first-order hidden Markov model. Finally, the optimal state sequence of the high-order hidden Markov model is inferred from the optimal state sequence of the equivalent first-order hidden Markov model. This method provides a unified algorithm

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10

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

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11

Sin, Bongkee, and Jin H. Kim. "Nonstationary hidden Markov model." Signal Processing 46, no. 1 (1995): 31–46. http://dx.doi.org/10.1016/0165-1684(95)00070-t.

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12

Shi, Fang Fang, Xian Yi Cheng, and Xiang Chen. "The Summarize of Improved HMM Model." Advanced Materials Research 756-759 (September 2013): 3384–88. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3384.

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The hidden markov model is a kind of important probability model of series data processing and statistical learning and it has been successfully applied in many engineering tasks. This paper introduces the basic principle of hidden markov model firstly, and then discusses the limitations of hidden markov model, as well as the improved hidden markov model which is put forward to solve these problems.

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13

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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14

Lee, Kyung-Ah, Dae-Jong Lee, Jang-Hwan Park, and Myung-Geun Chun. "Face Recognition Using Wavelet Coefficients and Hidden Markov Model." Journal of Korean Institute of Intelligent Systems 13, no. 6 (2003): 673–78. http://dx.doi.org/10.5391/jkiis.2003.13.6.673.

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15

Rose, Michael Del, Christian Wagner, and Philip Frederick. "Evidence Feed Forward Hidden Markov Model: A New Type Of Hidden Markov Model." International Journal of Artificial Intelligence & Applications 2, no. 1 (2011): 1–19. http://dx.doi.org/10.5121/ijaia.2011.2101.

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16

Nabil, M. Hewahi. "BRAIN Journal - Genetic Algorithms Principles Towards Hidden Markov Model." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 2, no. 3 (2011): 5–11. https://doi.org/10.5281/zenodo.1042614.

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ABSTRACT In this paper we propose a general approach based on Genetic Algorithms (GAs) to evolve Hidden Markov Models (HMM). The problem appears when experts assign probability values for HMM, they use only some limited inputs. The assigned probability values might not be accurate to serve in other cases related to the same domain. We introduce an approach based on GAs to find out the suitable probability values for the HMM to be mostly correct in more cases than what have been used to assign the probability values.

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17

Rai, Prerna, and Arvind Lal. "Google PageRank Algorithm: Markov Chain Model and Hidden Markov Model." International Journal of Computer Applications 138, no. 9 (2016): 9–13. http://dx.doi.org/10.5120/ijca2016908942.

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18

Yuan, Shenfang, Jinjin Zhang, Jian Chen, Lei Qiu, and Weibo Yang. "A uniform initialization Gaussian mixture model–based guided wave–hidden Markov model with stable damage evaluation performance." Structural Health Monitoring 18, no. 3 (2018): 853–68. http://dx.doi.org/10.1177/1475921718783652.

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During practical applications, the time-varying service conditions usually lead to difficulties in properly interpreting structural health monitoring signals. The guided wave–hidden Markov model–based damage evaluation method is a promising approach to address the uncertainties caused by the time-varying service condition. However, researches that have been performed to date are not comprehensive. Most of these research studies did not introduce serious time-varying factors, such as those that exist in reality, and hidden Markov model was applied directly without deep consideration of the perf

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19

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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20

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

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21

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

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22

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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23

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

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24

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

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25

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

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26

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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27

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

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28

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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29

SETIAWATY, B., and L. KRISTINA. "PENDUGAAN PARAMETER MODEL HIDDEN MARKOV *." Journal of Mathematics and Its Applications 4, no. 1 (2005): 23. http://dx.doi.org/10.29244/jmap.4.1.23-40.

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Pendugaan parameter untuk model Hidden Markov Elliott et. al. (1995) dilakukan mengunakan Metode Maximum Likelihood dan pendugaan ulang menggunakan metode Expectation Maximization yang melibatkan perubahan ukuran. Dari metode tersebut diperoleh algoritma untuk menduga parameter model.

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30

Jandera, Ales, and Tomas Skovranek. "Customer Behaviour Hidden Markov Model." Mathematics 10, no. 8 (2022): 1230. http://dx.doi.org/10.3390/math10081230.

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In this work, the Customer behaviour hidden Markov model (CBHMM) is proposed to predict the behaviour of customers in e-commerce with the goal to forecast the store income. The model consists of three sub-models: Vendor, Psychology and Loyalty, returning probabilities used in the transition matrix of the hidden Markov model, deciding upon three decision-states: “Order completed”, “Order uncompleted” or “No order”. The model outputs are read by the Viterbi algorithm to estimate if the order has been completed successfully, followed by the evaluation of the forecasted store income. The proposed

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31

Benyacoub, Badreddine, Souad ElBernoussi, Abdelhak Zoglat, and EL Moudden Ismail. "Classification with hidden Markov model." Applied Mathematical Sciences 8 (2014): 2483–96. http://dx.doi.org/10.12988/ams.2014.42129.

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32

Mulyami, Mirtawati, and Ali Ilham Sofiyat. "HIDDEN MARKOV MODEL DAN APLIKASINYA." Matematika Sains 2, no. 1 (2024): 46–52. http://dx.doi.org/10.34005/ms.v2i1.3803.

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This paper presents the results of research literature about the author of Hidden Markov Model (HMM). Theories about HMM developed for state that can not be observed directly, the model also remain hidden, but the model parameters are known and fixed output can be obtained. Application state HMM on weather observation within 7 days and hidden and not brought the state to bring a raincoat, using all five parameters: The transition matrix (A), the emission matrix (B), the number of hidden elements of state (N), the number of state observed (M) and the initial probability distribution (π). The ou

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33

van der Hoek, John, and Robert J. Elliott. "A modified hidden Markov model." Automatica 49, no. 12 (2013): 3509–19. http://dx.doi.org/10.1016/j.automatica.2013.09.012.

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34

Peng, Zhongxing, Wei Huang, and Yinghui Zhu. "Feedforward Factorial Hidden Markov Model." Mathematics 13, no. 7 (2025): 1201. https://doi.org/10.3390/math13071201.

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This paper introduces a novel kind of factorial hidden Markov model (FHMM), specifically the feedforward FHMM (FFHMM). In contrast to traditional FHMMs, the FFHMM is capable of directly utilizing supplementary information from observations through predefined states, which are derived using an automatic feature filter (AFF). We investigate two variations of FFHMM models that integrate predefined states with the FHMM: the direct FFHMM and the embedded FFHMM. In the direct FFHMM, alterations to one sub-hidden Markov model (HMM) do not affect the others, enabling individual improvements in HMM est

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35

Koide, Satoshi, Hiroshi Ohno, Ryuta Terashima, Thanomsak Ajjanapanya, and Itti Rittaporn. "Hidden Markov Flow Network Model: A Generative Model for Dynamic Flow on a Network." International Journal of Machine Learning and Computing 4, no. 4 (2014): 319–27. http://dx.doi.org/10.7763/ijmlc.2014.v4.431.

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36

Nguyen, Nguyet, Dung Nguyen, and Thomas P. Wakefield. "Using the Hidden Markov Model to Improve the Hull-White Model for Short Rate." International Journal of Trade, Economics and Finance 9, no. 2 (2018): 54–59. http://dx.doi.org/10.18178/ijtef.2018.9.2.588.

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37

Tseng, Din-Chang, and Ruei-Lung Chen. "Mutiscale Texture Segmentation Using Contextual Hidden Markov Tree Models." International Journal of Machine Learning and Computing 5, no. 3 (2015): 198–205. http://dx.doi.org/10.7763/ijmlc.2015.v5.507.

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38

Mitrophanov, Alexander Yu, Alexandre Lomsadze, and Mark Borodovsky. "Sensitivity of hidden Markov models." Journal of Applied Probability 42, no. 3 (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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39

Mitrophanov, Alexander Yu, Alexandre Lomsadze, and Mark Borodovsky. "Sensitivity of hidden Markov models." Journal of Applied Probability 42, no. 03 (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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40

Wang, Bing, Ping Yan, Qiang Zhou, and Libing Feng. "State recognition method for machining process of a large spot welder based on improved genetic algorithm and hidden Markov model." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 11 (2016): 2135–46. http://dx.doi.org/10.1177/0954406215626942.

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Large spot welder is an important equipment in rail transit equipment manufacturing industry, but having the problem of low utilization rate and low effectlvely machining rate. State monitoring can master its operating states real time and comprehensively, and providing data support for state recognition. Hidden Markov model is a state classification method, but it is sensitive to the initial model parameters and easy to trap into a local optima. Genetic algorithm is a global searching method; however, it is quite poor at hill climbing and also has the problem of premature convergence. In this

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41

Bredikhin, B. A., M. H. Antor, N. A. Khlebnikov, A. V. Melnikov та M. V. Bachurin. "Распознавание дизартричной речи по фонемам с использованием скрытых марковских моделей". МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ 12, № 1(44) (2024): 2. http://dx.doi.org/10.26102/2310-6018/2024.44.1.002.

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The relevance of the paper is due to the difficulties of oral interaction between people with speech disorders and normotypic interlocutors as well as the low quality of abnormal speech recognition by standard speech recognition systems and the inability to create a system capable of processing any speech disorders. In this regard, this article is aimed at developing a method for automatic recognition of dysarthric speech using a pre-trained neural network for recognizing phonemes and hidden Markov models for converting phonemes into text and subsequent correction of recognition results using

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42

Kwon, Hyun-Han. "Probabilistic Assessment of Drought Characteristics based on Homogeneous Hidden Markov Model." Journal of the Korean Society of Civil Engineers 34, no. 1 (2014): 145. http://dx.doi.org/10.12652/ksce.2014.34.1.0145.

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43

Sebastian, Tunny, Visalakshi Jeyaseelan, Lakshmanan Jeyaseelan, Shalini Anandan, Sebastian George, and Shrikant I. Bangdiwala. "Decoding and modelling of time series count data using Poisson hidden Markov model and Markov ordinal logistic regression models." Statistical Methods in Medical Research 28, no. 5 (2018): 1552–63. http://dx.doi.org/10.1177/0962280218766964.

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Hidden Markov models are stochastic models in which the observations are assumed to follow a mixture distribution, but the parameters of the components are governed by a Markov chain which is unobservable. The issues related to the estimation of Poisson-hidden Markov models in which the observations are coming from mixture of Poisson distributions and the parameters of the component Poisson distributions are governed by an m-state Markov chain with an unknown transition probability matrix are explained here. These methods were applied to the data on Vibrio cholerae counts reported every month

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44

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

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45

Kulkarni, Shrutanjay, Sanket Murkute, Prateek Nangare, Sameer Metkar, and Prof Mote R S. "ATM Fraud Detection using Hidden Markov Model Study of Hidden Markov Model and its Application." IJARCCE 6, no. 4 (2017): 14–17. http://dx.doi.org/10.17148/ijarcce.2017.6403.

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46

Vyshnavi, M., and M. Muthukumar. "COMPARATIVE ANALYSIS OF TRIANGULAR FUZZY HIDDEN MARKOV MODELS AND TRADITIONAL HIDDEN MARKOV MODELS." Advances and Applications in Statistics 92, no. 2 (2024): 171–89. https://doi.org/10.17654/0972361725009.

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This study optimizes Traditional Hidden Markov Models (THMMs) using Triangular Fuzzy Membership Functions, resulting in Triangular Fuzzy Hidden Markov Models (TFHMMs) that tolerate ambiguous observations and gradual state transitions in agricultural data prediction. Using oilseed area data from 1992 to 2022, we compare TFHMMs to traditional HMMs, focusing on predicting accuracy using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Corrected Akaike Information Criterion (AICc), and Hannan-Quinn Information Criterion (HQIC). Our research includes stationary paramete

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47

SETIAWATY, B. "A HIDDEN MARKOV MODEL: DEPENDENCIES BETWEEN RANDOM VARIABLES AND ITS REPRESENTATION." Journal of Mathematics and Its Applications 1, no. 2 (2002): 11. http://dx.doi.org/10.29244/jmap.1.2.11-22.

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<p>This article shows the nature of dependencies between random variables in a hidden Markov model. Using these properties,we will show that the law of a hidden Markov model is completely speciﬁed by a set of four parameters which is called a representation of the hidden Markov model. </p>

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48

Ankur, Singh Bist* Dr. Anuj Sharma. "CLASSIFICATION OF METAMORPHIC VIRUS USING HMM APPROACH." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 1 (2017): 330–38. https://doi.org/10.5281/zenodo.253984.

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Recent research work shows that HMM (Hidden Markov Model) is widely used in metamorphic virus detection. Virus generated from kits like NGVCK are detected effectively by HMM approach. Our purpose is to examine various flavours of HMM approach in virus detection.

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49

Suharleni, Farida, Agus Widodo, and Endang Wahyu H. "Hidden Markov Model Application to Transfer The Trader Online Forex Brokers." CAUCHY 2, no. 2 (2012): 66. http://dx.doi.org/10.18860/ca.v2i2.2222.

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<p>Hidden Markov Model is elaboration of Markov chain, which is applicable to cases that can’t directly observe. In this research, Hidden Markov Model is used to know trader’s transition to broker forex online. In Hidden Markov Model, observed state is observable part and hidden state is hidden part. Hidden Markov Model allows modeling system that contains interrelated observed state and hidden state. As observed state in trader’s transition to broker forex online is category 1, category 2, category 3, category 4, category 5 by condition of every broker forex online, whereas as hidden st

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50

YANG, XIAOYUAN, XUDONG ZHANG, and ZHIPIN ZHU. "FRAME-BASED IMAGE DENOISING USING HIDDEN MARKOV MODEL." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 03 (2008): 419–32. http://dx.doi.org/10.1142/s0219691308002446.

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Wavelet-domain hidden Markov models (HMMs), and in particular, hidden Markov tree (HMT), have been recently proposed and applied to image denoising. In this paper, we present the hidden Markov model and corresponding algorithm based on frame-domain. This model can effectively capture the correlation of wavelet frame coefficients, and we apply this model for image denoising. Furthermore, two new algorithms are developed for denoising. We demonstrate the performance of our new method on some text images with very encouraging results.

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