Journal articles: 'Hidden semi-Markov chains'

1

Guédon, Yann. "Hidden hybrid Markov/semi-Markov chains." Computational Statistics & Data Analysis 49, no. 3 (June 2005): 663–88. http://dx.doi.org/10.1016/j.csda.2004.05.033.

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Elliott, Robert, Nikolaos Limnios, and Anatoliy Swishchuk. "Filtering hidden semi-Markov chains." Statistics & Probability Letters 83, no. 9 (September 2013): 2007–14. http://dx.doi.org/10.1016/j.spl.2013.05.007.

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Guédon, Yann. "Estimating Hidden Semi-Markov Chains From Discrete Sequences." Journal of Computational and Graphical Statistics 12, no. 3 (September 2003): 604–39. http://dx.doi.org/10.1198/1061860032030.

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Guédon, Yann. "Computational methods for discrete hidden semi-Markov chains." Applied Stochastic Models in Business and Industry 15, no. 3 (July 1999): 195–224. http://dx.doi.org/10.1002/(sici)1526-4025(199907/09)15:3<195::aid-asmb376>3.0.co;2-f.

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Lapuyade-Lahorgue, Jérôme, and Wojciech Pieczynski. "Unsupervised segmentation of hidden semi-Markov non-stationary chains." Signal Processing 92, no. 1 (January 2012): 29–42. http://dx.doi.org/10.1016/j.sigpro.2011.06.001.

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Guédon, Yann. "Exploring the state sequence space for hidden Markov and semi-Markov chains." Computational Statistics & Data Analysis 51, no. 5 (February 2007): 2379–409. http://dx.doi.org/10.1016/j.csda.2006.03.015.

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Crowder, Martin. "Semi-Markov Chains and Hidden Semi-Markov Models Toward Applications by Vlad Stefan Barbu, Nikolaos Limnios." International Statistical Review 77, no. 2 (August 2009): 307. http://dx.doi.org/10.1111/j.1751-5823.2009.00085_8.x.

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Votsi, I., and N. Limnios. "Estimation of the intensity of the hitting time for semi-Markov chains and hidden Markov renewal chains." Journal of Nonparametric Statistics 27, no. 2 (February 6, 2015): 149–66. http://dx.doi.org/10.1080/10485252.2015.1009369.

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Lapuyade-Lahorgue, Jérôme, and Wojciech Pieczynski. "Unsupervised segmentation of new semi-Markov chains hidden with long dependence noise." Signal Processing 90, no. 11 (November 2010): 2899–910. http://dx.doi.org/10.1016/j.sigpro.2010.04.008.

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Negrón, C., M. L. Contador, B. D. Lampinen, S. G. Metcalf, Y. Guédon, E. Costes, and T. M. DeJong. "USING HIDDEN SEMI-MARKOV CHAINS TO COMPARE THE SHOOT STRUCTURE OF THREE DIFFERENT ALMOND CULTIVARS." Acta Horticulturae, no. 1068 (February 2015): 67–75. http://dx.doi.org/10.17660/actahortic.2015.1068.7.

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Guédon, Yann. "Semi-Markov Chains and Hidden Semi-Markov Models toward Applications: Their Use in Reliability and DNA Analysis by BARBU, V. S. and LIMNIOS, N." Biometrics 65, no. 4 (November 23, 2009): 1312. http://dx.doi.org/10.1111/j.1541-0420.2009.01343_8.x.

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Yuan, Yuan, Lei Lin, Jingbo Chen, Hichem Sahli, Yixiang Chen, Chengyi Wang, and Bin Wu. "A New Framework for Modelling and Monitoring the Conversion of Cultivated Land to Built-up Land Based on a Hierarchical Hidden Semi-Markov Model Using Satellite Image Time Series." Remote Sensing 11, no. 2 (January 21, 2019): 210. http://dx.doi.org/10.3390/rs11020210.

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Large amounts of farmland loss caused by urban expansion has been a severe global environmental problem. Therefore, monitoring urban encroachment upon farmland is a global issue. In this study, we propose a novel framework for modelling and monitoring the conversion of cultivated land to built-up land using a satellite image time series (SITS). The land-cover change process is modelled by a two-level hierarchical hidden semi-Markov model, which is composed of two Markov chains with hierarchical relationships. The upper chain represents annual land-cover dynamics, and the lower chain encodes the vegetation phenological patterns of each land-cover type. This kind of architecture enables us to represent the multilevel semantic information of SITS at different time scales. Specifically, intra-annual series reflect phenological differences and inter-annual series reflect land-cover dynamics. In this way, we can take advantage of the temporal information contained in the entire time series as well as the prior knowledge of land cover conversion to identify where and when changes occur. As a case study, we applied the proposed method for mapping annual, long-term urban-induced farmland loss from Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series in the Jing-Jin-Tang district, China from 2001 to 2010. The accuracy assessment showed that the proposed method was accurate for detecting conversions from cultivated land to built-up land, with the overall accuracy of 97.72% in the spatial domain and the temporal accuracy of 74.60%. The experimental results demonstrated the superiority of the proposed method in comparison with other state-of-the-art algorithms. In addition, the spatial-temporal patterns of urban expansion revealed in this study are consistent with the findings of previous studies, which also confirms the effectiveness of the proposed method.

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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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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.

15

Sujatha, R., V. Sharmila, and S. Narasimman. "Fuzzy hidden semi Markov chain with observation dependent states." Life Cycle Reliability and Safety Engineering 7, no. 1 (December 18, 2017): 1–9. http://dx.doi.org/10.1007/s41872-017-0034-4.

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Wang, Ning, Shu-dong Sun, Zhi-qiang Cai, Shuai Zhang, and Can Saygin. "A Hidden Semi-Markov Model with Duration-Dependent State Transition Probabilities for Prognostics." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/632702.

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Realistic prognostic tools are essential for effective condition-based maintenance systems. In this paper, a Duration-Dependent Hidden Semi-Markov Model (DD-HSMM) is proposed, which overcomes the shortcomings of traditional Hidden Markov Models (HMM), including the Hidden Semi-Markov Model (HSMM): (1) it allows explicit modeling of state transition probabilities between the states; (2) it relaxes observations’ independence assumption by accommodating a connection between consecutive observations; and (3) it does not follow the unrealistic Markov chain’s memoryless assumption and therefore it provides a more powerful modeling and analysis capability for real world problems. To facilitate the computation of the proposed DD-HSMM methodology, new forward-backward algorithm is developed. The demonstration and evaluation of the proposed methodology is carried out through a case study. The experimental results show that the DD-HSMM methodology is effective for equipment health monitoring and management.

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ELLIOTT, ROBERT J., and AHMED S. HAMADA. "OPTION PRICING USING A REGIME SWITCHING STOCHASTIC DISCOUNT FACTOR." International Journal of Theoretical and Applied Finance 17, no. 03 (May 2014): 1450020. http://dx.doi.org/10.1142/s0219024914500204.

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The paper discusses the pricing of derivatives using a stochastic discount factor modeled as a regime switching geometric Brownian motion. The regime switching is driven by a continuous time hidden Markov chain representing changes in the economy. The stochastic discount factor enables to define a risk neutral measure. We model the stock price as discounted future dividends driven by the same continuous time Markov chain. The stochastic discount factor is used to price European style options under the historical probability measure. The introduction of occupation times of the Markov chain and the corresponding conditional characteristic function allows the evaluation of the expected value of European type claims. The option price is given as a semi-analytical form using the Fourier transform.

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Chen, Zhe, Sujith Vijayan, Riccardo Barbieri, Matthew A. Wilson, and Emery N. Brown. "Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States." Neural Computation 21, no. 7 (July 2009): 1797–862. http://dx.doi.org/10.1162/neco.2009.06-08-799.

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UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process.

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Jouybari-Moghaddam, Y., M. R. Saradjian, and A. M. Forati. "A PROBABILITY MODEL FOR DROUGHT PREDICTION USING FUSION OF MARKOV CHAIN AND SAX METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 26, 2017): 101–4. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-101-2017.

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Drought is one of the most powerful natural disasters which are affected on different aspects of the environment. Most of the time this phenomenon is immense in the arid and semi-arid area. Monitoring and prediction the severity of the drought can be useful in the management of the natural disaster caused by drought. Many indices were used in predicting droughts such as SPI, VCI, and TVX. In this paper, based on three data sets (rainfall, NDVI, and land surface temperature) which are acquired from MODIS satellite imagery, time series of SPI, VCI, and TVX in time limited between winters 2000 to summer 2015 for the east region of Isfahan province were created. Using these indices and fusion of symbolic aggregation approximation and hidden Markov chain drought was predicted for fall 2015.<br><br> For this purpose, at first, each time series was transformed into the set of quality data based on the state of drought (5 group) by using SAX algorithm then the probability matrix for the future state was created by using Markov hidden chain.<br><br> The fall drought severity was predicted by fusion the probability matrix and state of drought severity in summer 2015. The prediction based on the likelihood for each state of drought includes severe drought, middle drought, normal drought, severe wet and middle wet. The analysis and experimental result from proposed algorithm show that the product of this algorithm is acceptable and the proposed algorithm is appropriate and efficient for predicting drought using remote sensor data.

20

Costes, E., and Y. Guédon. "Modeling the Sylleptic Branching on One-year-old Trunks of Apple Cultivars." Journal of the American Society for Horticultural Science 122, no. 1 (January 1997): 53–62. http://dx.doi.org/10.21273/jashs.122.1.53.

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The structure of 1-year-old trunks resulting from sylleptic branching are compared among apple (Malus domestica Borkh) cultivars with diverse branching and fruiting habits. The 1-year-old trunks developing from a graft are described as a succession of metamers whose structure refers to location, distribution, and length of sylleptic axillary shoots. We used a stochastic process called hidden semi-Markov chain to capture the embedded structure resulting from mixing of different types of axillary shoots developing along the trunks. The models, corresponding to the different cultivars, are composed of a first transient nonbranched state, a succession of transient states that cover the median sylleptic branching zone, and a final absorbing nonbranched state. They are interpreted with regard to complexity, extent, and branching distribution of the median sylleptic zone. Main results deal with the balance between long and short sylleptic shoots and the distribution of long sylleptic shoots along the trunks. Results suggest that sylleptic branching could be used as an early characteristic to evaluate the later branching behavior of cultivars.

21

Fernandes, Clément, and Wojciech Pieczynski. "Non-stationary data segmentation with hidden evidential semi-Markov chains." International Journal of Approximate Reasoning, September 2023, 109025. http://dx.doi.org/10.1016/j.ijar.2023.109025.

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22

Olivier, Brice, Anne Guérin-Dugué, and Jean-Baptiste Durand. "Hidden semi-Markov models to segment reading phases from eye movements." Journal of Eye Movement Research 15, no. 4 (September 30, 2022). http://dx.doi.org/10.16910/jemr.15.4.5.

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Our objective is to analyze scanpaths acquired through participants achieving a reading task aiming at answering a binary question: Is the text related or not to some given target topic? We propose a data-driven method based on hidden semi-Markov chains to segment scanpaths into phases deduced from the model states, which are shown to represent different cognitive strategies: normal reading, fast reading, information search, and slow confirmation. These phases were confirmed using different external covariates, among which semantic information extracted from texts. Analyses highlighted some strong preference of specific participants for specific strategies and more globally, large individual variability in eye-movement characteristics, as accounted for by random effects. As a perspective, the possibility of improving reading models by accounting for possible heterogeneity sources during reading is discussed.

23

Dama, Fatoumata, and Christine Sinoquet. "Partially Hidden Markov Chain Multivariate Linear Autoregressive model: inference and forecasting—application to machine health prognostics." Machine Learning, November 28, 2022. http://dx.doi.org/10.1007/s10994-022-06209-5.

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AbstractTime series subject to regime shifts have attracted much interest in domains such as econometry, finance or meteorology. For discrete-valued regimes, models such as the popular Hidden Markov Chain (HMC) describe time series whose state process is unknown at all time-steps. Sometimes, time series are annotated. Thus, another category of models handles the case with regimes observed at all time-steps. We present a novel model which addresses the intermediate case: (i) state processes associated to such time series are modelled by Partially Hidden Markov Chains (PHMCs); (ii) a multivariate linear autoregressive (MLAR) model drives the dynamics of the time series, within each regime. We describe a variant of the expectation maximization (EM) algorithm devoted to PHMC-MLAR model learning. We propose a hidden state inference procedure and a forecasting function adapted to the semi-supervised framework. We first assess inference and prediction performances, and analyze EM convergence times for PHMC-MLAR, using simulated data. We show the benefits of using partially observed states as well as a fully labelled scheme with unreliable labels, to decrease EM convergence times. We highlight the robustness of PHMC-MLAR to labelling errors in inference and prediction tasks. Finally, using turbofan engine data from a NASA repository, we show that PHMC-MLAR outperforms or largely outperforms other models: PHMC and MSAR (Markov Switching AutoRegressive model) for the feature prediction task, PHMC and five out of six recent state-of-the-art methods for the prediction of machine useful remaining life.

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Kuljus, Kristi, and Jüri Lember. "Pairwise Markov Models and Hybrid Segmentation Approach." Methodology and Computing in Applied Probability 25, no. 2 (June 2023). http://dx.doi.org/10.1007/s11009-023-10044-z.

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AbstractThe article studies segmentation problem (also known as classification problem) with pairwise Markov models (PMMs). A PMM is a process where the observation process and underlying state sequence form a two-dimensional Markov chain, it is a natural generalization of a hidden Markov model. To demonstrate the richness of the class of PMMs, we examine closer a few examples of rather different types of PMMs: a model for two related Markov chains, a model that allows to model an inhomogeneous Markov chain as a conditional marginal process of a homogeneous PMM, and a semi-Markov model. The segmentation problem assumes that one of the marginal processes is observed and the other one is not, the problem is to estimate the unobserved state path given the observations. The standard state path estimators often used are the so-called Viterbi path (a sequence with maximum state path probability given the observations) or the pointwise maximum a posteriori (PMAP) path (a sequence that maximizes the conditional state probability for given observations pointwise). Both these estimators have their limitations, therefore we derive formulas for calculating the so-called hybrid path estimators which interpolate between the PMAP and Viterbi path. We apply the introduced algorithms to the studied models in order to demonstrate the properties of different segmentation methods, and to illustrate large variation in behaviour of different segmentation methods in different PMMs. The studied examples show that a segmentation method should always be chosen with care by taking into account the purpose of modelling and the particular model of interest.

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Kim, Joungyoun, Johan Lim, and Jong Soo Lee. "Semi-parametric hidden Markov model for large-scale multiple testing under dependency." Statistical Modelling, September 27, 2022, 1471082X2211212. http://dx.doi.org/10.1177/1471082x221121235.

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In this article, we propose a new semiparametric hidden Markov model (HMM) for use in the simultaneous hypothesis testing with dependency. The semi- or non-parametric HMM in the literature requires two conditions for its model identifiability, (a) the latent Markov chain (MC) is ergodic and its transition probability is full rank and (b) the observational distributions of different hidden states are disjoint or linearly independent. Unlike the existing models, our semiparametric HMM with two hidden states makes no assumption on the transition probability of the latent MC but assumes that observational distributions are extremal for the set of all stationary distributions of the model. To estimate the model, we propose a modified expectation-maximization algorithm, whose M-step has an additional purification step to make the observational distribution be extremal one. We numerically investigate the performance of the proposed procedure in the estimation of the model and compare it to two recent existing methods in various multiple testing error settings. In addition, we apply our procedure to analyzing two real data examples, the gas chromatography/mass spectrometry experiment to differentiate the origin of herbal medicine and the epidemiologic surveillance of an influenza-like illness.

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Zararsız, Zarife. "Decoding and Re-estimation of fuzzy hidden semi Markov chain with observation dependent state." Journal of Industrial and Management Optimization, 2023, 0. http://dx.doi.org/10.3934/jimo.2023065.

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27

Gámiz, María Luz, Nikolaos Limnios, and Mari Carmen Segovia-García. "The continuous-time hidden Markov model based on discretization. Properties of estimators and applications." Statistical Inference for Stochastic Processes, June 23, 2023. http://dx.doi.org/10.1007/s11203-023-09292-0.

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AbstractIn this paper we consider continuous-time hidden Markov processes (CTHMM). The model considered is a two-dimensional stochastic process $$(X_t,Y_t)$$ ( X t , Y t ) , with $$X_t$$ X t an unobserved (hidden) Markov chain defined by its generating matrix and $$Y_t$$ Y t an observed process whose distribution law depends on $$X_t$$ X t and is called the emission function. In general, we allow the process $$Y_t$$ Y t to take values in a subset of the q-dimensional real space, for some q. The coupled process $$(X_t,Y_t)$$ ( X t , Y t ) is a continuous-time Markov chain whose generator is constructed from the generating matrix of X and the emission distribution. We study the theoretical properties of this two-dimensional process using a formulation based on semi-Markov processes. Observations of the CTHMM are obtained by discretization considering two different scenarii. In the first case we consider that observations of the process Y are registered regularly in time, while in the second one, observations arrive at random. Maximum-likelihood estimators of the characteristics of the coupled process are obtained in both scenarii and the asymptotic properties of these estimators are shown, such as consistency and normality. To illustrate the model a real-data example and a simulation study are considered.

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Agunwamba, Jonah, Michael Toryila Tiza, and Fidelis Okafor. "An appraisal of statistical and probabilistic models in highway pavements." Turkish Journal of Engineering, February 17, 2024. http://dx.doi.org/10.31127/tuje.1389994.

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Accurate performance prediction is crucial for safe and efficient travel on highway pavements. Within pavement engineering, statistical models play a pivotal role in understanding pavement behavior and durability. This comprehensive study critically evaluates a spectrum of statistical models utilized in pavement engineering, encompassing mechanistic-empirical, Weibull distribution, Markov chain, regression, Bayesian networks, Monte Carlo simulation, artificial neural networks, support vector machines, random forest, decision tree, fuzzy logic, time series analysis, stochastic differential equations, copula, hidden semi-Markov, generalized linear, survival analysis, response surface methodology and extreme value theory models. The assessment meticulously examines equations, parameters, data prerequisites, advantages, limitations, and applicability of each model. Detailed discussions delve into the significance of equations and parameters, evaluating model performance in predicting pavement distress, performance assessment, design optimization, and life-cycle cost analysis. Key findings emphasize the critical aspects of accurate input parameters, calibration, validation, data availability, and model complexity. Strengths, limitations, and applicability across various pavement types, materials, and climate conditions are meticulously highlighted for each model. Recommendations are outlined to enhance the effectiveness of statistical models in pavement engineering. These suggestions encompass further research and development, standardized data collection, calibration and validation protocols, model integration, decision-making frameworks, collaborative efforts, and ongoing model evaluation. Implementing these recommendations is anticipated to enhance prediction accuracy and enable informed decision-making throughout highway pavement design, construction, maintenance, and management. This study is anticipated to serve as a valuable resource, providing guidance and insights for researchers, practitioners, and stakeholders engaged in asphalt engineering, facilitating the effective utilization of statistical models in real-world pavement projects.

Journal articles on the topic 'Hidden semi-Markov chains'

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