Relevant bibliographies by topics / Hiden Markov model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Hiden Markov model'

Author: Grafiati
Published: 1 July 2022
Last updated: 19 July 2025

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Journal articles on the topic "Hiden Markov model"

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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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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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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 model P(O|) can solved by Forward-Backward algorithm, the decoding problem or to choose hidden state sequence which is optimal can solved by Viterbi algorithm and learning problem or to estimate hidden Markov model parameter to maximize P(O|) can solved by Baum – Welch algorithm. From description above Hidden Markov Model with state 3 can describe behavior from the case studies. Key words: Decoding Problem, Evaluation Problem, Hidden Markov Model, Learning Problem
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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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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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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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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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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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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 framework for decoding hidden Markov models including the first-order hidden Markov model and any high-order hidden Markov model.
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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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Dissertations / Theses on the topic "Hiden Markov model"

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Devilliers, Esther. "Modélisation micro-économétrique des choix de pratiques de production et des utilisations d'intrants chimiques des agriculteurs : une approche par les fonctions de production latentes." Thesis, Rennes, Agrocampus Ouest, 2021. http://www.theses.fr/2021NSARE058.

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La notion d’itinéraire technique est une notion agronomique qui nous permet d’appréhender l’imbrication entre les rendements objectifs et les niveaux d’utilisation d’intrants associés. Dès lors, on peut admettre qu’à différents types d’itinéraires techniques correspondent différentes fonctions de production. Modéliser ces différentes fonctions est une des clés pour mieux comprendre la dépendance de certaines pratiques culturales aux pesticides et de ce fait constitue un enjeu majeur pour concevoir les futures politiques publiques.Intégrer cette notion d’itinéraire technique nécessite de tenir compte de l’interdépendance entre les choix de ces pratiques, leur rendement et les utilisations associées. Pour ce faire, on considère des modèles de changement de régime endogène qui permettent de contrôler des biais de sélection. Lorsque ces pratiques sont inobservées, on définit la séquence de choix comme processus Markovien.Le modèle résultant nous permet de recouvrir les pratiques culturales, leurs niveaux de rendement et d’utilisation d’intrants ainsi que la dynamique de choix des dites pratiques. Lorsque ces pratiques sont observées, on décide de considérer un modèle primal afin de pouvoir vérifier le rôle différencié des pesticides et évaluer l’effet des politiques publiques conjointement sur les rendements et les niveaux d’utilisation d’intrants chimiques.En bref, cette thèse vise à donner des outils pour évaluer au mieux les effets des politiques agro-environnementales sur les utilisations de pesticides, les rendements et mes choix de pratiques culturales des agriculteurs<br>Cropping management practices is an agronomic notion grasping the interdependence between targeted yield and input use levels. Subsequently, one can legitimately assume that different cropping management practices are associated to different production functions. To better understand pesticide dependence – a key point to encourage more sustainable practices – one have to consider modelling cropping management practices specific production functions.Because of the inherent interdependence between those practices and their associeted yield and input use levels, we need to consider endogenous regime switching models.When unobserved, the sequence of cropping management practices choices is considered as a Markovian process. From this modelling framework we can derive the cropping management choices, their dynamics, their associated yield and input use levels. When observed, we consider primal production functions to see how yield responds differently to input uses based on the different cropping management practices. Thus, we can assess jointly the effect of a public policy on input use and yield levels.In a nutshell, in this PhD we are aiming at giving some tools to evaluate the differentiated effect of agri-environmental public policies on production choies and on the associated yield and input use levels
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Kotsalis, Georgios. "Model reduction for Hidden Markov models." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38255.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.<br>Includes bibliographical references (leaves 57-60).<br>The contribution of this thesis is the development of tractable computational methods for reducing the complexity of two classes of dynamical systems, finite alphabet Hidden Markov Models and Jump Linear Systems with finite parameter space. The reduction algorithms employ convex optimization and numerical linear algebra tools and do not pose any structural requirements on the systems at hand. In the Jump Linear Systems case, a distance metric based on randomization of the parametric input is introduced. The main point of the reduction algorithm lies in the formulation of two dissipation inequalities, which in conjunction with a suitably defined storage function enable the derivation of low complexity models, whose fidelity is controlled by a guaranteed upper bound on the stochastic L2 gain of the approximation error. The developed reduction procedure can be interpreted as an extension of the balanced truncation method to the broader class of Jump Linear Systems. In the Hidden Markov Model case, Hidden Markov Models are identified with appropriate Jump Linear Systems that satisfy certain constraints on the coefficients of the linear transformation. This correspondence enables the development of a two step reduction procedure.<br>(cont.) In the first step, the image of the high dimensional Hidden Markov Model in the space of Jump Linear Systems is simplified by means of the aforementioned balanced truncation method. Subsequently, in the second step, the constraints that reflect the Hidden Markov Model structure are imposed by solving a low dimensional non convex optimization problem. Numerical simulation results provide evidence that the proposed algorithm computes accurate reduced order Hidden Markov Models, while achieving a compression of the state space by orders of magnitude.<br>by Georgios Kotsalis.<br>Ph.D.
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Kadhem, Safaa K. "Model fit diagnostics for hidden Markov models." Thesis, University of Plymouth, 2017. http://hdl.handle.net/10026.1/9966.

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Hidden Markov models (HMMs) are an efficient tool to describe and model the underlying behaviour of many phenomena. HMMs assume that the observed data are generated independently from a parametric distribution, conditional on an unobserved process that satisfies the Markov property. The model selection or determining the number of hidden states for these models is an important issue which represents the main interest of this thesis. Applying likelihood-based criteria for HMMs is a challenging task as the likelihood function of these models is not available in a closed form. Using the data augmentation approach, we derive two forms of the likelihood function of a HMM in closed form, namely the observed and the conditional likelihoods. Subsequently, we develop several modified versions of the Akaike information criterion (AIC) and Bayesian information criterion (BIC) approximated under the Bayesian principle. We also develop several versions for the deviance information criterion (DIC). These proposed versions are based on the type of likelihood, i.e. conditional or observed likelihood, and also on whether the hidden states are dealt with as missing data or additional parameters in the model. This latter point is referred to as the concept of focus. Finally, we consider model selection from a predictive viewpoint. To this end, we develop the so-called widely applicable information criterion (WAIC). We assess the performance of these various proposed criteria via simulation studies and real-data applications. In this thesis, we apply Poisson HMMs to model the spatial dependence analysis in count data via an application to traffic safety crashes for three highways in the UK. The ultimate interest is in identifying highway segments which have distinctly higher crash rates. Selecting an optimal number of states is an important part of the interpretation. For this purpose, we employ model selection criteria to determine the optimal number of states. We also use several goodness-of-fit checks to assess the model fitted to the data. We implement an MCMC algorithm and check its convergence. We examine the sensitivity of the results to the prior specification, a potential problem given small sample sizes. The Poisson HMMs adopted can provide a different model for analysing spatial dependence on networks. It is possible to identify segments with a higher posterior probability of classification in a high risk state, a task that could prioritise management action.
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Bulla, Jan. "Application of Hidden Markov and Hidden Semi-Markov Models to Financial Time Series." Doctoral thesis, [S.l. : s.n.], 2006. http://swbplus.bsz-bw.de/bsz260867136inh.pdf.

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Wynne-Jones, Michael. "Model building in neural networks with hidden Markov models." Thesis, University of Edinburgh, 1994. http://hdl.handle.net/1842/284.

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This thesis concerns the automatic generation of architectures for neural networks and other pattern recognition models comprising many elements of the same type. The requirement for such models, with automatically determined topology and connectivity, arises from two needs. The first is the need to develop commercial applications of the technology without resorting to laborious trial and error with different network sizes; the second is the need, in large and complex pattern processing applications such as speech recognition, to optimise the allocation of computing resources for problem solving. The state of the art in adaptive architectures is reviewed, and a mechanism is proposed for adding new processing elements to models. The scheme is developed in the context of multi-layer perceptron networks, and is linked to the best network-pruning mechanism available using a numerical criterion with construction required at one extreme and pruning at the other. The construction mechanism does not work in the multi-layer perceptron for which it was developed, owing to the long-range effects occurring in many applications of these networks. It works demonstrably well in density estimation models based on Gaussian mixtures, which are of the same family as the increasingly popular radial basis function networks. The construction mechanism is applied to the initialization of the density estimators embedded in the states of a hidden Markov model for speaker-independent speech recognition, where it offers a considerable increase in recogniser performance, provided cross-validation is used to prevent over-training.
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Tillman, Måns. "On-Line Market Microstructure Prediction Using Hidden Markov Models." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208312.

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Over the last decades, financial markets have undergone dramatic changes. With the advent of the arbitrage pricing theory, along with new technology, markets have become more efficient. In particular, the new high-frequency markets, with algorithmic trading operating on micro-second level, make it possible to translate ”information” into price almost instantaneously. Such phenomena are studied in the field of market microstructure theory, which aims to explain and predict them. In this thesis, we model the dynamics of high frequency markets using non-linear hidden Markov models (HMMs). Such models feature an intuitive separation between observations and dynamics, and are therefore highly convenient tools in financial settings, where they allow a precise application of domain knowledge. HMMs can be formulated based on only a few parameters, yet their inherently dynamic nature can be used to capture well-known intra-day seasonality effects that many other models fail to explain. Due to recent breakthroughs in Monte Carlo methods, HMMs can now be efficiently estimated in real-time. In this thesis, we develop a holistic framework for performing both real-time inference and learning of HMMs, by combining several particle-based methods. Within this framework, we also provide methods for making accurate predictions from the model, as well as methods for assessing the model itself. In this framework, a sequential Monte Carlo bootstrap filter is adopted to make on-line inference and predictions. Coupled with a backward smoothing filter, this provides a forward filtering/backward smoothing scheme. This is then used in the sequential Monte Carlo expectation-maximization algorithm for finding the optimal hyper-parameters for the model. To design an HMM specifically for capturing information translation, we adopt the observable volume imbalance into a dynamic setting. Volume imbalance has previously been used in market microstructure theory to study, for example, price impact. Through careful selection of key model assumptions, we define a slightly modified observable as a process that we call scaled volume imbalance. The outcomes of this process retain the key features of volume imbalance (that is, its relationship to price impact and information), and allows an efficient evaluation of the framework, while providing a promising platform for future studies. This is demonstrated through a test on actual financial trading data, where we obtain high-performance predictions. Our results demonstrate that the proposed framework can successfully be applied to the field of market microstructure.<br>Under de senaste decennierna har det gjorts stora framsteg inom finansiell teori för kapitalmarknader. Formuleringen av arbitrageteori medförde möjligheten att konsekvent kunna prissätta finansiella instrument. Men i en tid då högfrekvenshandel numera är standard, har omsättningen av information i pris börjat ske i allt snabbare takt. För att studera dessa fenomen; prispåverkan och informationsomsättning, har mikrostrukturteorin vuxit fram. I den här uppsatsen studerar vi mikrostruktur med hjälp av en dynamisk modell. Historiskt sett har mikrostrukturteorin fokuserat på statiska modeller men med hjälp av icke-linjära dolda Markovmodeller (HMM:er) utökar vi detta till den dynamiska domänen. HMM:er kommer med en naturlig uppdelning mellan observation och dynamik, och är utformade på ett sådant sätt att vi kan dra nytta av domänspecifik kunskap. Genom att formulera lämpliga nyckelantaganden baserade på traditionell mikrostrukturteori specificerar vi en modell—med endast ett fåtal parametrar—som klarar av att beskriva de välkända säsongsbeteenden som statiska modeller inte klarar av. Tack vare nya genombrott inom Monte Carlo-metoder finns det nu kraftfulla verktyg att tillgå för att utföra optimal filtrering med HMM:er i realtid. Vi applicerar ett så kallat bootstrap filter för att sekventiellt filtrera fram tillståndet för modellen och prediktera framtida tillstånd. Tillsammans med tekniken backward smoothing estimerar vi den posteriora simultana fördelningen för varje handelsdag. Denna används sedan för statistisk inlärning av våra hyperparametrar via en sekventiell Monte Carlo Expectation Maximization-algoritm. För att formulera en modell som beskriver omsättningen av information, väljer vi att utgå ifrån volume imbalance, som ofta används för att studera prispåverkan. Vi definierar den relaterade observerbara storheten scaled volume imbalance som syftar till att bibehålla kopplingen till prispåverkan men även går att modellera med en dynamisk process som passar in i ramverket för HMM:er. Vi visar även hur man inom detta ramverk kan utvärdera HMM:er i allmänhet, samt genomför denna analys för vår modell i synnerhet. Modellen testas mot finansiell handelsdata för både terminskontrakt och aktier och visar i bägge fall god predikteringsförmåga.
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Hofer, Gregor Otto. "Speech-driven animation using multi-modal hidden Markov models." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/3786.

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The main objective of this thesis was the synthesis of speech synchronised motion, in particular head motion. The hypothesis that head motion can be estimated from the speech signal was confirmed. In order to achieve satisfactory results, a motion capture data base was recorded, a definition of head motion in terms of articulation was discovered, a continuous stream mapping procedure was developed, and finally the synthesis was evaluated. Based on previous research into non-verbal behaviour basic types of head motion were invented that could function as modelling units. The stream mapping method investigated in this thesis is based on Hidden Markov Models (HMMs), which employ modelling units to map between continuous signals. The objective evaluation of the modelling parameters confirmed that head motion types could be predicted from the speech signal with an accuracy above chance, close to 70%. Furthermore, a special type ofHMMcalled trajectoryHMMwas used because it enables synthesis of continuous output. However head motion is a stochastic process therefore the trajectory HMM was further extended to allow for non-deterministic output. Finally the resulting head motion synthesis was perceptually evaluated. The effects of the “uncanny valley” were also considered in the evaluation, confirming that rendering quality has an influence on our judgement of movement of virtual characters. In conclusion a general method for synthesising speech-synchronised behaviour was invented that can applied to a whole range of behaviours.
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McKee, Bill Frederick. "Optimal hidden Markov models." Thesis, University of Plymouth, 1999. http://hdl.handle.net/10026.1/1698.

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In contrast with training algorithms such as Baum-Welch, which produce solutions that are a local optimum of the objective function, this thesis describes the attempt to develop a training algorithm which delivers the global optimum Discrete ICdden Markov Model for a given training sequence. A total of four different methods of attack upon the problem are presented. First, after building the necessary analytical tools, the thesis presents a direct, calculus-based assault featuring Matrix Derivatives. Next, the dual analytic approach known as Geometric Programming is examined and then adapted to the task. After that, a hill-climbing formula is developed and applied. These first three methods reveal a number of interesting and useful insights into the problem. However, it is the fourth method which produces an algorithm that is then used for direct comparison vAth the Baum-Welch algorithm: examples of global optima are collected, examined for common features and patterns, and then a rule is induced. The resulting rule is implemented in *C' and tested against a battery of Baum-Welch based programs. In the limited range of tests carried out to date, the models produced by the new algorithm yield optima which have not been surpassed by (and are typically much better than) the Baum-Welch models. However, far more analysis and testing is required and in its current form the algorithm is not fast enough for realistic application.
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Schimert, James. "A high order hidden Markov model /." Thesis, Connect to this title online; UW restricted, 1992. http://hdl.handle.net/1773/8939.

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Chong, Fong Ho. "Frequency-stream-tying hidden Markov model /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202003%20CHONG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 119-123). Also available in electronic version. Access restricted to campus users.
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Books on the topic "Hiden Markov model"

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Westhead, David R., and M. S. Vijayabaskar, eds. Hidden Markov Models. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6753-7.

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Bouguila, Nizar, Wentao Fan, and Manar Amayri, eds. Hidden Markov Models and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99142-5.

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Koski, Timo. Hidden Markov Models for Bioinformatics. Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0612-5.

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Mamon, Rogemar S., and Robert J. Elliott, eds. Hidden Markov Models in Finance. Springer US, 2007. http://dx.doi.org/10.1007/0-387-71163-5.

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Mamon, Rogemar S., and Robert J. Elliott, eds. Hidden Markov Models in Finance. Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7442-6.

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Cappé, Olivier, Eric Moulines, and Tobias Rydén. Inference in Hidden Markov Models. Springer New York, 2005. http://dx.doi.org/10.1007/0-387-28982-8.

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Eric, Moulines, and Rydén Tobias 1966-, eds. Inference in hidden Markov models. Springer, 2005.

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Cappe, Olivier. Inference in hidden Markov models. Springer, 2005.

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Elliott, Robert J., and Rogemar S. Mamon. Hidden Markov models in finance. Springer, 2011.

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Zucchini, Walter, Iain L. MacDonald, and Roland Langrock. Hidden Markov Models for Time Series. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b20790.

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Book chapters on the topic "Hiden Markov model"

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Awad, Mariette, and Rahul Khanna. "Hidden Markov Model." In Efficient Learning Machines. Apress, 2015. http://dx.doi.org/10.1007/978-1-4302-5990-9_5.

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Li, Hang. "Hidden Markov Model." In Machine Learning Methods. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_10.

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Blanco-Castañeda, Liliana, and Viswanathan Arunachalam. "Hidden Markov Model." In Applied Stochastic Modeling. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31282-3_5.

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Wu, Jun. "Hidden Markov model." In The Beauty of Mathematics in Computer Science. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315169491-5.

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Privault, Nicolas. "Hidden Markov Model." In Springer Undergraduate Mathematics Series. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65820-4_9.

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Sucar, Luis Enrique. "Hidden Markov Models." In Probabilistic Graphical Models. Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6699-3_5.

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Sucar, Luis Enrique. "Hidden Markov Models." In Probabilistic Graphical Models. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61943-5_5.

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Coelho, João Paulo, Tatiana M. Pinho, and José Boaventura-Cunha. "Autoregressive Markov Models." In Hidden Markov Models. CRC Press, 2019. http://dx.doi.org/10.1201/9780429261046-5.

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Fink, Gernot A. "Hidden Markov Models." In Markov Models for Pattern Recognition. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6308-4_5.

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Coelho, João Paulo, Tatiana M. Pinho, and José Boaventura-Cunha. "Discrete Hidden Markov Models." In Hidden Markov Models. CRC Press, 2019. http://dx.doi.org/10.1201/9780429261046-3.

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Conference papers on the topic "Hiden Markov model"

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Citron, Levli, Kobi Cohen, and Qing Zhao. "Anomaly Search of a Hidden Markov Model." In 2024 IEEE International Symposium on Information Theory (ISIT). IEEE, 2024. http://dx.doi.org/10.1109/isit57864.2024.10619616.

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Farshchian, Maryam, and Majid Vafaei Jahan. "Stock market prediction with Hidden Markov Model." In 2015 International Congress on Technology, Communication and Knowledge (ICTCK). IEEE, 2015. http://dx.doi.org/10.1109/ictck.2015.7582714.

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Somani, Poonam, Shreyas Talele, and Suraj Sawant. "Stock market prediction using Hidden Markov Model." In 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2014. http://dx.doi.org/10.1109/itaic.2014.7065011.

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Gupta, Aditya, and Bhuwan Dhingra. "Stock market prediction using Hidden Markov Models." In 2012 Students Conference on Engineering and Systems (SCES). IEEE, 2012. http://dx.doi.org/10.1109/sces.2012.6199099.

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Gupta, Kriti. "Hidden Markov Model." In the International Conference & Workshop. ACM Press, 2011. http://dx.doi.org/10.1145/1980022.1980326.

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Dimoulkas, Ilias, Mikael Amelin, and Mohammad Reza Hesamzadeh. "Forecasting balancing market prices using Hidden Markov Models." In 2016 13th International Conference on the European Energy Market (EEM). IEEE, 2016. http://dx.doi.org/10.1109/eem.2016.7521229.

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Ben Ayed, Alaidine, and S. Selouani. "Market customers classification using Hidden Markov Models toolkit." In 2013 International Conference on Computer Applications Technology (ICCAT 2013). IEEE, 2013. http://dx.doi.org/10.1109/iccat.2013.6521974.

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Saxena, Rishabh, Adarsh Upadhayay, Gaurav Raj, et al. "Analysis of Enhanced Hidden Markov Models for Improved Stock Market Price Forecasting and Prediction." In AICCONF '24: Cognitive Models and Artificial Intelligence Conference. ACM, 2024. http://dx.doi.org/10.1145/3660853.3660922.

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Kotsalis, Georgios, Alexandre Megretski, and Munther A. Dahleh. "A Model Reduction Algorithm for Hidden Markov Models." In Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006. http://dx.doi.org/10.1109/cdc.2006.377011.

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Hassan, M. R., and B. Nath. "Stock market forecasting using hidden Markov model: a new approach." In 5th International Conference on Intelligent Systems Design and Applications (ISDA'05). IEEE, 2005. http://dx.doi.org/10.1109/isda.2005.85.

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Reports on the topic "Hiden Markov model"

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Ghahramani, Zoubin, and Michael I. Jordan. Factorial Hidden Markov Models. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada307097.

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Ainsleigh, Phillip L. Theory of Continuous-State Hidden Markov Models and Hidden Gauss-Markov Models. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada415930.

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Yang, Jie, and Yangsheng Xu. Hidden Markov Model for Gesture Recognition. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada282845.

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Thrun, Sebastian, and John Langford. Monte Carlo Hidden Markov Models. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada363714.

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Hollis, Andrew, George Tompkins, Alyson Wilson, and Ralph Smith. Proliferation Monitoring with Hidden Markov Models. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1766975.

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Ricca, Bernard. Nonlinear Time Series Analyses (Part II). Instats Inc., 2024. https://doi.org/10.61700/0emwhpkidie951390.

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Abstract:
This four-day seminar delves into phenomenological modeling of time series, focusing on nonlinear dynamic methodologies such as sparse identification of nonlinear dynamics, dynamic mode decomposition, and hidden Markov models, all implemented using the R programming language. Participants will enhance their research skills by learning to dynamically model, analyze, and interpret time-dependent data, ultimately enabling them to conduct innovative and robust research across various fields.
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Del Rose, Michael S., Philip Frederick, and Christian Wagner. Using Evidence Feed-Forward Hidden Markov Models. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada543331.

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Balasubramanian, Vijay. Equivalence and Reduction of Hidden Markov Models. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada270762.

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Chan, A. D., K. Englehart, B. Hudgins, and D. F. Lovely. Hidden Markov Model Classification of Myoelectric Signals in Speech. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada410037.

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Baggenstoss, Paul M. A Multi-Resolution Hidden Markov Model Using Class-Specific Features. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada494596.

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