Gotowe bibliografie tematyczne / Hidden Markov process

Gotowa bibliografia na temat „Hidden Markov process”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 31 stycznia 2023

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Artykuły w czasopismach na temat "Hidden Markov process"

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Alshraideh, Hussam, i George Runger. "Process Monitoring Using Hidden Markov Models". Quality and Reliability Engineering International 30, nr 8 (2.09.2013): 1379–87. http://dx.doi.org/10.1002/qre.1560.

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Wang, Bing, Ping Yan, Qiang Zhou i 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, nr 11 (27.01.2016): 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 paper, proposing the improved genetic algorithm, and combining improved genetic algorithm and hidden Markov model, a new method of state recognition method named improved genetic algorithm–hidden Markov model is proposed. In the proposed method, improved genetic algorithm is used for optimizing the initial parameters, and hidden Markov model as a classifier to recognize the operating states for machining process. This method is also compared with the other two recognition methods named adaptive genetic algorithm–hidden Markov model and hidden Markov model, in which adaptive genetic algorithm is similarly used for optimizing the initial parameters, however hidden Markov model (in both methods) as a classifier. Experimental results show that the proposed method is very effective, and the improved genetic algorithm–hidden Markov model recognition method is superior to the adaptive genetic algorithm–hidden Markov model and hidden Markov model recognition method.
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Xu, Yangsheng, i Ming Ge. "Hidden Markov model-based process monitoring system". Journal of Intelligent Manufacturing 15, nr 3 (czerwiec 2004): 337–50. http://dx.doi.org/10.1023/b:jims.0000026572.03164.64.

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Elkimakh, Karima, i Abdelaziz Nasroallah. "Hidden Markov Model with Markovian emission". Monte Carlo Methods and Applications 26, nr 4 (1.12.2020): 303–13. http://dx.doi.org/10.1515/mcma-2020-2072.

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AbstractIn our paper [A. Nasroallah and K. Elkimakh, HMM with emission process resulting from a special combination of independent Markovian emissions, Monte Carlo Methods Appl. 23 2017, 4, 287–306] we have studied, in a first scenario, the three fundamental hidden Markov problems assuming that, given the hidden process, the observed one selects emissions from a combination of independent Markov chains evolving at the same time. Here, we propose to conduct the same study with a second scenario assuming that given the hidden process, the emission process selects emissions from a combination of independent Markov chain evolving according to their own clock. Three basic numerical examples are studied to show the proper functioning of the iterative algorithm adapted to the proposed model.
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Yu, Feng-Hui, Wai-Ki Ching, Jia-Wen Gu i Tak-Kuen Siu. "Interacting default intensity with a hidden Markov process". Quantitative Finance 17, nr 5 (7.11.2016): 781–94. http://dx.doi.org/10.1080/14697688.2016.1237036.

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Zuk, Or, Ido Kanter i Eytan Domany. "The Entropy of a Binary Hidden Markov Process". Journal of Statistical Physics 121, nr 3-4 (listopad 2005): 343–60. http://dx.doi.org/10.1007/s10955-005-7576-y.

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Ko, Stanley I. M., Terence T. L. Chong i Pulak Ghosh. "Dirichlet Process Hidden Markov Multiple Change-point Model". Bayesian Analysis 10, nr 2 (czerwiec 2015): 275–96. http://dx.doi.org/10.1214/14-ba910.

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Jacquet, Philippe, Gadiel Seroussi i Wojciech Szpankowski. "On the entropy of a hidden Markov process". Theoretical Computer Science 395, nr 2-3 (maj 2008): 203–19. http://dx.doi.org/10.1016/j.tcs.2008.01.012.

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Wu, Hongmin, Yisheng Guan i Juan Rojas. "Analysis of multimodal Bayesian nonparametric autoregressive hidden Markov models for process monitoring in robotic contact tasks". International Journal of Advanced Robotic Systems 16, nr 2 (1.03.2019): 172988141983484. http://dx.doi.org/10.1177/1729881419834840.

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Robot introspection aids robots to understand what they do and how they do it. Previous robot introspection techniques have often used parametric hidden Markov models or supervised learning techniques, implying that the number of hidden states or classes is defined a priori and fixed through the entire modeling process. Fixed parameterizations limit the modeling power of a process to properly encode the data. Furthermore, first-order Markov models are limited in their ability to model complex data sequences that represent highly dynamic behaviors as they assume observations are conditionally independent given the state. In this work, we contribute a Bayesian nonparametric autoregressive Hidden Markov model for the monitoring of robot contact tasks, which are characterized by complex dynamical data that are hard to model. We used a nonparametric prior that endows our hidden Markov models with an unbounded number of hidden states for a given robot skill (or subtask). We use a hierarchical Dirichlet stochastic process prior to learn an hidden Markov model with a switching vector autoregressive observation model of wrench signatures and end-effector pose for the manipulation contact tasks. The proposed scheme monitors both nominal skill execution and anomalous behaviors. Two contact tasks are used to measure the effectiveness of our approach: (i) a traditional pick-and-place task composed of four skills and (ii) a cantilever snap assembly task (also composed of four skills). The modeling performance or our approach was compared with other methods, and classification accuracy measures were computed for skill and anomaly identification. The hierarchical Dirichlet stochastic process prior to learn an hidden Markov model with a switching vector autoregressive observation model was shown to have excellent process monitoring performance with higher identification rates and monitoring ability.
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Qi-feng, Yao, Dong Yun i Wang Zhong-Zhi. "An Entropy Rate Theorem for a Hidden Inhomogeneous Markov Chain". Open Statistics & Probability Journal 8, nr 1 (30.09.2017): 19–26. http://dx.doi.org/10.2174/1876527001708010019.

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Objective: The main object of our study is to extend some entropy rate theorems to a Hidden Inhomogeneous Markov Chain (HIMC) and establish an entropy rate theorem under some mild conditions. Introduction: A hidden inhomogeneous Markov chain contains two different stochastic processes; one is an inhomogeneous Markov chain whose states are hidden and the other is a stochastic process whose states are observable. Materials and Methods: The proof of theorem requires some ergodic properties of an inhomogeneous Markov chain, and the flexible application of the properties of norm and the bounded conditions of series are also indispensable. Results: This paper presents an entropy rate theorem for an HIMC under some mild conditions and two corollaries for a hidden Markov chain and an inhomogeneous Markov chain. Conclusion: Under some mild conditions, the entropy rates of an inhomogeneous Markov chains, a hidden Markov chain and an HIMC are similar and easy to calculate.
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Rozprawy doktorskie na temat "Hidden Markov process"

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Jin, Chao. "A Sequential Process Monitoring Approach using Hidden Markov Model for Unobservable Process Drift". University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341969.

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Mattila, Robert. "Hidden Markov models : Identification, control and inverse filtering". Licentiate thesis, KTH, Reglerteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223683.

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The hidden Markov model (HMM) is one of the workhorse tools in, for example, statistical signal processing and machine learning. It has found applications in a vast number of fields, ranging all the way from bioscience to speech recognition to modeling of user interactions in social networks. In an HMM, a latent state transitions according to Markovian dynamics. The state is only observed indirectly via a noisy sensor – that is, it is hidden. This type of model is at the center of this thesis, which in turn touches upon three main themes. Firstly, we consider how the parameters of an HMM can be estimated from data. In particular, we explore how recently proposed methods of moments can be combined with more standard maximum likelihood (ML) estimation procedures. The motivation for this is that, albeit the ML estimate possesses many attractive statistical properties, many ML schemes have to rely on local-search procedures in practice, which are only guaranteed to converge to local stationary points in the likelihood surface – potentially inhibiting them from reaching the ML estimate. By combining the two types of algorithms, the goal is to obtain the benefits of both approaches: the consistency and low computational complexity of the former, and the high statistical efficiency of the latter. The filtering problem – estimating the hidden state of the system from observations – is of fundamental importance in many applications. As a second theme, we consider inverse filtering problems for HMMs. In these problems, the setup is reversed; what information about an HMM-filtering system is exposed by its state estimates? We show that it is possible to reconstruct the specifications of the sensor, as well as the observations that were made, from the filtering system’s posterior distributions of the latent state. This can be seen as a way of reverse engineering such a system, or as using an alternative data source to build a model. Thirdly, we consider Markov decision processes (MDPs) – systems with Markovian dynamics where the parameters can be influenced by the choice of a control input. In particular, we show how it is possible to incorporate prior information regarding monotonic structure of the optimal decision policy so as to accelerate its computation. Subsequently, we consider a real-world application by investigating how these models can be used to model the treatment of abdominal aortic aneurysms (AAAs). Our findings are that the structural properties of the optimal treatment policy are different than those used in clinical practice – in particular, that younger patients could benefit from earlier surgery. This indicates an opportunity for improved care of patients with AAAs.

QC 20180301

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Chamroukhi, Faicel. "Hidden process regression for curve modeling, classification and tracking". Compiègne, 2010. http://www.theses.fr/2010COMP1911.

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Cette thèse s'est focalisée sur l'analyse de courbes à changements de régime. Nous proposons de nouvelles approches probabilistes génératives pour modéliser, classer et suivre temporellement de telles courbes. Le premier volet de la thèse concerne la modélisation et la classification (supervisée ou non) d'un ensemble de courbes indépendantes. Les approches proposées dans ce cadre, qui peuvent être appliquées aussi bien à une courbe qu'à un ensemble de courbes, reposent sur un modèle de régression spécifique incorporant un processus caché s'adaptant aussi bien à des changements de régimes brusques qu'à des changements lents. Le second volet de la thèse concerne la modélisation dynamique d'une séquence de courbes à changements de régime. Nous proposons pour cela des modèles autorégressifs intégrant eux même un processus caché et dont l'apprentissage est réalisé à la fois en mode "hors ligne", quand les courbes sont stockées à l'avance, et en mode "en ligne", quand les courbes arrivent au fur et à mesure au cours du temps. Le volet applicatif de la thèse concerne le diagnostic et le suivi d'état de fonctionnement du mécanisme d'aiguillage des rails qui est un organe impactant considérablement la disponibilité du réseau ferroviaire. Sa surveillance est essentielle pour mieux planifier les actions de maintenance. Les données disponibles pour réaliser cette tâche sont les courbes de puissance électrique acquises lors des manœuvres d'aiguillage, qui ont notamment la particularité de présenter des changements de régime. Les résultats obtenus sur des courbes simulées et des courbes acquises lors de manœuvres d'aiguillage illustrent l'utilité pratique des approches introduites dans cette thèse
This research addresses the problem of diagnosis and monitoring for predictive maintenance of the railway infrastructure. In particular, the switch mechanism is a vital organ because its operating state directly impacts the overall safety of the railway system and its proper functioning is required for the full availability of the transportation system; monitoring it is a key task within maintenance team actions. To monitor and diagnose the switch mechanism, the main available data are curves of electric power acquired during several switch operations. This study therefore focuses on modeling curve-valued or functional data presenting regime changes. In this thesis we propose new probabilistic generative machine learning methodologies for curve modeling, classification, clustering and tracking. First, the models we propose for a single curve or independent sets of curves are based on specific regression models incorporating a flexible hidden process. They are able to capture non-stationary (dynamic) behavior within the curves and address the problem of missing information regarding the underlying regimes, and the problem of complex shaped classes. We then propose dynamic models for learning from curve sequences to make decision and prediction over time. The developed approaches rely on autoregressive dynamic models governed by hidden processes. The learning of the models is performed in both a batch mode (in which the curves are stored in advance) and an online mode as the learning proceeds (in which the curves are analyzed one at a time). The obtained results on both simulated curves and the real world switch operation curves demonstrate the practical use of the ideas introduced in this thesis
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Balali, Samaneh. "Incorporating expert judgement into condition based maintenance decision support using a coupled hidden markov model and a partially observable markov decision process". Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=19510.

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Preventive maintenance consists of activities performed to maintain a system in a satisfactory functional condition. Condition Based Maintenance (CBM) aims to reduce the cost of preventive maintenance by supporting decisions on performing maintenance actions, based on information reflecting a system's health condition. In practice, the condition related information can be obtained in various ways, including continuous condition monitoring performed by sensors, or subjective assessment performed by humans. An experienced engineer might provide such subjective assessment by visually inspecting a system, or by interpreting the data collected by condition monitoring devices, and hence give an 'expert judgement' on the state of the system. There is limited academic literature on the development of CBM models incorporating expert judgement. This research aims to reduce this gap by developing models that formally incorporate expert judgement into the CBM decisi on process. A Coupled Hidden Markov Model is proposed to model the evolutionary relationship between expert judgement and the true deterioration state of a system. This model is used to estimate the underlying condition of the system and predict the remaining time to failure. A training algorithm is developed to support model parameter estimation. The algorithm's performance is evaluated with respect to the number of expert judgements and initial settings of model parameters. A decision-making problem is formulated to account for the use of expert judgement in selecting maintenance actions in light of the physical investigation of the system's condition. A Partially Observable Markov Decision Process is proposed to recommend the most cost-effective decisions on inspection choice and maintenance action in two consecutive steps. An approximate method is developed to solve the proposed decision optimisation model and obtain the optimal policy. The sensitivity of the optimal policy is evaluated with respect to model parameters settings, such as the accuracy of the expert judgement.
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Wong, Wee Chin. "Estimation and control of jump stochastic systems". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31775.

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Thesis (Ph.D)--Chemical Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Jay H. Lee; Committee Member: Alexander Gray; Committee Member: Erik Verriest; Committee Member: Magnus Egerstedt; Committee Member: Martha Grover; Committee Member: Matthew Realff. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Löhr, Wolfgang. "Models of Discrete-Time Stochastic Processes and Associated Complexity Measures". Doctoral thesis, Universitätsbibliothek Leipzig, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-38267.

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Many complexity measures are defined as the size of a minimal representation in a specific model class. One such complexity measure, which is important because it is widely applied, is statistical complexity. It is defined for discrete-time, stationary stochastic processes within a theory called computational mechanics. Here, a mathematically rigorous, more general version of this theory is presented, and abstract properties of statistical complexity as a function on the space of processes are investigated. In particular, weak-* lower semi-continuity and concavity are shown, and it is argued that these properties should be shared by all sensible complexity measures. Furthermore, a formula for the ergodic decomposition is obtained. The same results are also proven for two other complexity measures that are defined by different model classes, namely process dimension and generative complexity. These two quantities, and also the information theoretic complexity measure called excess entropy, are related to statistical complexity, and this relation is discussed here. It is also shown that computational mechanics can be reformulated in terms of Frank Knight's prediction process, which is of both conceptual and technical interest. In particular, it allows for a unified treatment of different processes and facilitates topological considerations. Continuity of the Markov transition kernel of a discrete version of the prediction process is obtained as a new result.
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Damian, Camilla, Zehra Eksi-Altay i Rüdiger Frey. "EM algorithm for Markov chains observed via Gaussian noise and point process information: Theory and case studies". De Gruyter, 2018. http://dx.doi.org/10.1515/strm-2017-0021.

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In this paper we study parameter estimation via the Expectation Maximization (EM) algorithm for a continuous-time hidden Markov model with diffusion and point process observation. Inference problems of this type arise for instance in credit risk modelling. A key step in the application of the EM algorithm is the derivation of finite-dimensional filters for the quantities that are needed in the E-Step of the algorithm. In this context we obtain exact, unnormalized and robust filters, and we discuss their numerical implementation. Moreover, we propose several goodness-of-fit tests for hidden Markov models with Gaussian noise and point process observation. We run an extensive simulation study to test speed and accuracy of our methodology. The paper closes with an application to credit risk: we estimate the parameters of a hidden Markov model for credit quality where the observations consist of rating transitions and credit spreads for US corporations.
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Löhr, Wolfgang. "Models of Discrete-Time Stochastic Processes and Associated Complexity Measures". Doctoral thesis, Max Planck Institut für Mathematik in den Naturwissenschaften, 2009. https://ul.qucosa.de/id/qucosa%3A11017.

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Many complexity measures are defined as the size of a minimal representation in a specific model class. One such complexity measure, which is important because it is widely applied, is statistical complexity. It is defined for discrete-time, stationary stochastic processes within a theory called computational mechanics. Here, a mathematically rigorous, more general version of this theory is presented, and abstract properties of statistical complexity as a function on the space of processes are investigated. In particular, weak-* lower semi-continuity and concavity are shown, and it is argued that these properties should be shared by all sensible complexity measures. Furthermore, a formula for the ergodic decomposition is obtained. The same results are also proven for two other complexity measures that are defined by different model classes, namely process dimension and generative complexity. These two quantities, and also the information theoretic complexity measure called excess entropy, are related to statistical complexity, and this relation is discussed here. It is also shown that computational mechanics can be reformulated in terms of Frank Knight''s prediction process, which is of both conceptual and technical interest. In particular, it allows for a unified treatment of different processes and facilitates topological considerations. Continuity of the Markov transition kernel of a discrete version of the prediction process is obtained as a new result.
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Carvalho, Walter Augusto Fonsêca de 1964. "Processos de renovação obtidos por agregação de estados a partir de um processo markoviano". [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/306196.

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Orientadores: Nancy Lopes Garcia, Alexsandro Giacomo Grimbert Gallo
Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica
Made available in DSpace on 2018-08-24T12:54:22Z (GMT). No. of bitstreams: 1 Carvalho_WalterAugustoFonsecade_D.pdf: 1034671 bytes, checksum: 25dd72305f343655bedfde62a785a259 (MD5) Previous issue date: 2014
Resumo: Esta tese é dedicada ao estudo dos processos de renovação binários obtidos como agregação de estados a partir de processos Markovianos com alfabeto finito. Na primeira parte, utilizamos uma abordagem matricial para obter condições sob as quais o processo agregado pertence a cada uma das seguintes classes: (1) Markoviano de ordem finita, (2) processo de ordem infinita com probabilidades de transição contínuas, (3) processo Gibbsiano. A segunda parte trata da distância d entre processos de renovação binários. Obtivemos condições sob as quais esta distância pode ser atingida entre tais processos
Abstract: This thesis is devoted to the study of binary renewal processes obtained as aggregation of states from Markov processes with finite alphabet. In the rst part, we use a matrix approach to obtain conditions under which the aggregated process belongs to each of the following classes: (1) Markov of finite order, (2) process of infinite order with continuous transition probabilities, (3) Gibbsian process. The second part deals with the distance d between binary renewal processes. We obtain conditions under which this distance can be achieved between these processes
Doutorado
Estatistica
Doutor em Estatística
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Starke, Martin, Benjamin Beck, Denis Ritz, Frank Will i Jürgen Weber. "Frequency based efficiency evaluation - from pattern recognition via backwards simulation to purposeful drive design". Technische Universität Dresden, 2020. https://tud.qucosa.de/id/qucosa%3A71072.

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The efficiency of hydraulic drive systems in mobile machines is influenced by several factors, like the operators’ guidance, weather conditions, material respectively loading properties and primarily the working cycle. This leads to varying operation points, which have to be performed by the drive system. Regarding efficiency analysis, the usage of standardized working cycles gained through measurements or synthetically generated is state of the art. Thereby, only a small extract of the real usage profile is taken into account. This contribution deals with process pattern recognition (PPR) and frequency based efficiency evaluation to gain more precise information and conclusion for the drive design of mobile machines. By the example of an 18 t mobile excavator, the recognition system using Hidden – Markov - Models (HMM) and the efficiency evaluation process by means of backwards simulation of measured operation points will be described.
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Książki na temat "Hidden Markov process"

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Eric, Moulines, i Rydén Tobias 1966-, red. Inference in hidden Markov models. New York: Springer, 2005.

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Hidden Markov models for bioinformatics. Dordrecht: Kluwer Academic Publishers, 2001.

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Elliott, Robert J. Hidden Markov models: Estimation and control. New York: Springer-Verlag, 1995.

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Hidden Markov models and dynamical systems. Philadelphia: Society for Industrial and Applied Mathematics, 2008.

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Elliott, Robert J., i Rogemar S. Mamon. Hidden Markov models in finance. New York: Springer, 2011.

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Grobel, Kirsti. Videobasierte Gebärdenspracherkennung mit Hidden-Markov-Modellen. Düsseldorf: VDI Verlag, 1999.

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Huang, X. D. Hidden Markov models for speech recognition. Edinburgh: Edinburgh University Press, 1990.

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Gollery, Martin. Handbook of hidden Markov models in bioinformatics. Boca Raton: Chapman & Hall/CRC, 2008.

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Gollery, Martin. Handbook of hidden Markov models in bioinformatics. Boca Raton: CRC Press, 2008.

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Bhar, Ramaprasad. Hidden Markov models: Applications to financial economics. Boston, Mass: Kluwer Academic Publishers, 2004.

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Części książek na temat "Hidden Markov process"

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Visser, Ingmar, Maartje E. J. Raijmakers i Han L. J. van der Maas. "Hidden Markov Models for Individual Time Series". W Dynamic Process Methodology in the Social and Developmental Sciences, 269–89. New York, NY: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-95922-1_13.

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Zhou, Ding, Yuanjun Gao i Liam Paninski. "Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model". W Machine Learning and Knowledge Discovery in Databases, 612–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67658-2_35.

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Carrera, Berny, i Jae-Yoon Jung. "Constructing Probabilistic Process Models Based on Hidden Markov Models for Resource Allocation". W Business Process Management Workshops, 477–88. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15895-2_41.

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Roger, Vincent, Marius Bartcus, Faicel Chamroukhi i Hervé Glotin. "Unsupervised Bioacoustic Segmentation by Hierarchical Dirichlet Process Hidden Markov Model". W Multimedia Tools and Applications for Environmental & Biodiversity Informatics, 113–30. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76445-0_7.

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Adomi, Masahiro, Yumi Shikauchi i Shin Ishii. "Hidden Markov Model for Human Decision Process in a Partially Observable Environment". W Artificial Neural Networks – ICANN 2010, 94–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15822-3_12.

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Manouchehri, Narges, i Nizar Bouguila. "Multivariate Beta-Based Hierarchical Dirichlet Process Hidden Markov Models in Medical Applications". W Unsupervised and Semi-Supervised Learning, 235–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99142-5_10.

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Baghdadi, Ali, Narges Manouchehri, Zachary Patterson i Nizar Bouguila. "Shifted-Scaled Dirichlet-Based Hierarchical Dirichlet Process Hidden Markov Models with Variational Inference Learning". W Unsupervised and Semi-Supervised Learning, 263–92. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-030-99142-5_11.

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Özyurt, I. Burak, Aydin K. Sunol i Lawrence O. Hall. "Chemical process fault diagnosis using kernel retrofitted fuzzy genetic algorithm based learner (FGAL) with a hidden Markov model". W Lecture Notes in Computer Science, 190–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64582-9_748.

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Sachin Krishnan, P., K. Rameshkumar i P. Krishnakumar. "Hidden Markov Modelling of High-Speed Milling (HSM) Process Using Acoustic Emission (AE) Signature for Predicting Tool Conditions". W Lecture Notes in Mechanical Engineering, 573–80. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1307-7_65.

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Sperotto, Anna, Ramin Sadre, Pieter-Tjerk de Boer i Aiko Pras. "Hidden Markov Model Modeling of SSH Brute-Force Attacks". W Integrated Management of Systems, Services, Processes and People in IT, 164–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04989-7_13.

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Streszczenia konferencji na temat "Hidden Markov process"

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Hamada, Ryunosuke, Takatomi Kubo, Kentaro Katahira, Kenta Suzuki, Kazuo Okanoya i Kazushi Ikeda. "Birdsong analysis using beta process hidden Markov model". W 2014 IEEE 24th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2014. http://dx.doi.org/10.1109/mlsp.2014.6958848.

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Papavieros, George, Ioannis Kontoyiannis, Vassilios Constantoudis i Evangelos Gogolides. "Denoising line edge roughness measurement using hidden Markov models". W Metrology, Inspection, and Process Control for Microlithography XXXIII, redaktorzy Ofer Adan i Vladimir A. Ukraintsev. SPIE, 2019. http://dx.doi.org/10.1117/12.2523422.

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Gao, Qing-Bin, i Shi-Liang Sun. "Human activity recognition with beta process hidden Markov models". W 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890353.

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Kang, Yihuang, i Vladimir Zadorozhny. "Process Discovery Using Classification Tree Hidden Semi-Markov Model". W 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI). IEEE, 2016. http://dx.doi.org/10.1109/iri.2016.55.

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Li, Zhijun, Jiang Zhong, Cunwu Han i Dehui Sun. "Process fault detection based on continuous hidden Markov model". W 2017 Chinese Automation Congress (CAC). IEEE, 2017. http://dx.doi.org/10.1109/cac.2017.8243244.

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Khodabandelou, Ghazaleh, Charlotte Hug, Rebecca Deneckere i Camille Salinesi. "Supervised intentional process models discovery using Hidden Markov models". W 2013 IEEE Seventh International Conference on Research Challenges in Information Science (RCIS). IEEE, 2013. http://dx.doi.org/10.1109/rcis.2013.6577711.

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Choukri, Imane, Hatim Guermah, Abdelmajid Daosabah i Mahmoud Nassar. "Context aware Hidden Markov Model for Intention process mining". W 2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS). IEEE, 2021. http://dx.doi.org/10.1109/icds53782.2021.9626765.

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Yoon, Hyung-Jin, Donghwan Lee i Naira Hovakimyan. "Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process". W 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8814849.

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Chen, J., i C. J. Hsu. "A Self-Growing Hidden Markov Tree for Batch Process Monitoring". W 2007 2nd IEEE Conference on Industrial Electronics and Applications. IEEE, 2007. http://dx.doi.org/10.1109/iciea.2007.4318786.

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Wang, Xiaofeng, Wei Ou i Jinshu Su. "A reputation inference model based on linear hidden markov process". W 2009 ISECS International Colloquium on Computing, Communication, Control, and Management (CCCM). IEEE, 2009. http://dx.doi.org/10.1109/cccm.2009.5270424.

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