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Journal articles on the topic 'First order Markov model'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

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

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

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2

Shen, Wei. "On the First-Order Markov Model for the PLC Fading Channel." Advanced Materials Research 756-759 (September 2013): 2644–48. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2644.

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The power line communications (PLC) channel is noisy one, which can be modeled by the Markov process. The order is the key concerning when used the Markov process. the level of complexity will be incurred from using higher order , while the first-order Markov models may lead to the less accurate channel response. In the paper, the first-order Markov channel is under thoroughly discussion, and it can provide a mathematically tractable model for time-varying channels and uses only the received SNR of the symbol immediately preceding the current one. With the first-order Markov chain, given the i

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3

Hawkins, Daniel B. "A First-Order Markov-Chain Model of Zeolite Crystallization1." Clays and Clay Minerals 37, no. 5 (1989): 433–38. http://dx.doi.org/10.1346/ccmn.1989.0370506.

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Dahan, Fadl, Ameur Touir, and Hassan Mathkour. "First Order Hidden Markov Model for Automatic Arabic Name Entity Recognition." International Journal of Computer Applications 123, no. 7 (2015): 37–40. http://dx.doi.org/10.5120/ijca2015905397.

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5

Eggar, M. H. "Validity of fitting a first-order Markov chain model to data." Journal of the Royal Statistical Society: Series D (The Statistician) 51, no. 2 (2002): 259–65. http://dx.doi.org/10.1111/1467-9884.00316.

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6

Korte, Alvin O. "A First Order Markov Model for Use in the Human Services." Computers in Human Services 6, no. 4 (1990): 299–312. http://dx.doi.org/10.1300/j407v06n04_07.

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Pati, Avadh, Awadhesh Kumar, and Dinesh Chandra. "Suboptimal Control Using Model Order Reduction." Chinese Journal of Engineering 2014 (February 3, 2014): 1–5. http://dx.doi.org/10.1155/2014/797581.

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A Padé approximation based technique for designing a suboptimal controller is presented. The technique uses matching of both time moments and Markov parameters for model order reduction. In this method, the suboptimal controller is first derived for reduced order model and then implemented for higher order plant by partial feedback of measurable states.

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Toivonen, Jarkko, Pratyush K. Das, Jussi Taipale, and Esko Ukkonen. "MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs." Bioinformatics 36, no. 9 (2020): 2690–96. http://dx.doi.org/10.1093/bioinformatics/btaa045.

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Abstract Motivation Position-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing. Results We pre

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Yu, Yi, and Haiquan Zhao. "A joint-optimization NSAF algorithm based on the first-order Markov model." Signal, Image and Video Processing 11, no. 3 (2016): 509–16. http://dx.doi.org/10.1007/s11760-016-0988-0.

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10

Demirekler, M. "On the first order equalization of hidden Markov models." IEEE Transactions on Signal Processing 43, no. 6 (1995): 1522–24. http://dx.doi.org/10.1109/78.388869.

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Wohlberg, B. E., and G. de Jager. "Fractal coding performance for first-order Gauss–Markov models." Electronics Letters 32, no. 5 (1996): 441. http://dx.doi.org/10.1049/el:19960306.

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12

du Preez, J. A. "Efficient training of high-order hidden Markov models using first-order representations." Computer Speech & Language 12, no. 1 (1998): 23–39. http://dx.doi.org/10.1006/csla.1997.0037.

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13

Wang, Hongjun, Zhen Yang, and Yingchun Shi. "Next Location Prediction Based on an Adaboost-Markov Model of Mobile Users." Sensors 19, no. 6 (2019): 1475. http://dx.doi.org/10.3390/s19061475.

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As an emerging class of spatial trajectory data, mobile user trajectory data can be used to analyze individual or group behavioral characteristics, hobbies and interests. Besides, the information extracted from original trajectory data is widely used in smart cities, transportation planning, and anti-terrorism maintenance. In order to identify the important locations of the target user from his trajectory data, a novel division method for preprocessing trajectory data is proposed, the feature points of original trajectory are extracted according to the change of trajectory structural, and then

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Kundu, Amlan, Yang He, and Paramvir Bahl. "Recognition of handwritten word: First and second order hidden Markov model based approach." Pattern Recognition 22, no. 3 (1989): 283–97. http://dx.doi.org/10.1016/0031-3203(89)90076-9.

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15

Parlar, Mahmut. "Stochastic control of a pension fund model with first-order Markov-dependent parameters." Optimal Control Applications and Methods 2, no. 2 (2007): 175–89. http://dx.doi.org/10.1002/oca.4660020206.

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16

Kuai, Le, Xiao-ya Fei, Jia-qi Xing, et al. "An Efficacy Predictive Method for Diabetic Ulcers Based on Higher-Order Markov Chain-Set Pair Analysis." Evidence-Based Complementary and Alternative Medicine 2020 (June 16, 2020): 1–19. http://dx.doi.org/10.1155/2020/5091671.

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Background. Clinical comprehensive decision-making of diabetic ulcers includes curative effect evaluation and curative effect prediction. Nevertheless, there are few studies on the prediction of diabetic ulcers. Methods. Set pair analysis (SPA) was used to assess the curative effect evaluation, and therapeutic effect was evaluated by connection degree (CD). The higher-order Markov chain-SPA curative effect prediction model was established to predict the future curative effect development. The predicted results with higher-order Markov chain-SPA and traditional first-order Markov-SPA model were

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Shirgave, Suresh, Prakash Kulkarni, and José Borges. "Semantically Enriched Variable Length Markov Chain Model for Analysis of User Web Navigation Sessions." International Journal of Information Technology & Decision Making 13, no. 04 (2014): 721–53. http://dx.doi.org/10.1142/s0219622014500643.

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The rapid growth of the World Wide Web has resulted in intricate Web sites, demanding enhanced user skills to find the required information and more sophisticated tools that are able to generate apt recommendations. Markov Chains have been widely used to generate next-page recommendations; however, accuracy of such models is limited. Herein, we propose the novel Semantic Variable Length Markov Chain Model (SVLMC) that combines the fields of Web Usage Mining and Semantic Web by enriching the Markov transition probability matrix with rich semantic information extracted from Web pages. We show th

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Schoof, J. T., and S. C. Pryor. "On the Proper Order of Markov Chain Model for Daily Precipitation Occurrence in the Contiguous United States." Journal of Applied Meteorology and Climatology 47, no. 9 (2008): 2477–86. http://dx.doi.org/10.1175/2008jamc1840.1.

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Abstract Markov chains are widely used tools for modeling daily precipitation occurrence. Given the assumption that the Markov chain model is the right model for daily precipitation occurrence, the choice of Markov model order was examined on a monthly basis for 831 stations in the contiguous United States using long-term data. The model order was first identified using the Bayesian information criteria (BIC). The maximum-likelihood estimates of the Markov transition probabilities were computed from 100 bootstrapped samples and were then used to generate 50-yr precipitation occurrence series.

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KATSIRI, ELEFTHERIA, JEAN BACON, and ALAN MYCROFT. "LINKING TEMPORAL FIRST ORDER LOGIC AND HIDDEN MARKOV MODELS WITH ABSTRACT EVENTS." International Journal on Artificial Intelligence Tools 19, no. 06 (2010): 857–93. http://dx.doi.org/10.1142/s0218213010000443.

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In previous work, we introduced a novel concept of a generalised event, an abstract event, which we define as a change of state of abstract predicates that represent knowledge about the surrounding world. Abstract predicates are defined by formulae in temporal first-order logic (Abstract Event Specification Language (AESL)) whose leaf predicates represent low-level sensor-derived knowledge. Abstract events are detected by Rete Networks structured as a deductive knowledge-base. Current Abstract Event detectors cannot express sufficiently well certain high-level situations, such activity derived

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Rahman, M. Sayedur. "A stochastic simulated first-order Markov chain model for daily rainfall at Barind, Bangladesh." Journal of Interdisciplinary Mathematics 2, no. 1 (1999): 7–32. http://dx.doi.org/10.1080/09720502.1999.10700255.

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21

Yang, Hongxing, Yutong Li, Lin Lu, and Ronghui Qi. "First order multivariate Markov chain model for generating annual weather data for Hong Kong." Energy and Buildings 43, no. 9 (2011): 2371–77. http://dx.doi.org/10.1016/j.enbuild.2011.05.035.

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22

Akyuz, Dilek Eren, Mehmetcik Bayazit, and Bihrat Onoz. "Markov Chain Models for Hydrological Drought Characteristics." Journal of Hydrometeorology 13, no. 1 (2012): 298–309. http://dx.doi.org/10.1175/jhm-d-11-019.1.

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Abstract Estimation of drought characteristics such as probabilities and return periods of droughts of various lengths is of major importance in drought forecast and management and in solving water resources problems related to water quality and navigation. This study aims at applying first- and second-order Markov chain models to dry and wet periods of annual streamflow series to reproduce the stochastic structure of hydrological droughts. Statistical evaluation of drought duration and intensity is usually carried out using runs analysis. First-order Markov chain model (MC1) for dry and wet p

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Juang, B. H., and K. K. Paliwal. "Hidden Markov models with first-order equalization for noisy speech recognition." IEEE Transactions on Signal Processing 40, no. 9 (1992): 2136–43. http://dx.doi.org/10.1109/78.157214.

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24

Chen, Zhen Zi, and Yi Chen. "A Customer Credit Visual Analytics Model Based on Markov Logic Networks." Key Engineering Materials 474-476 (April 2011): 1874–80. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1874.

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Customer credit evaluation is very important for customer relationship management in Enterprise Resource Planning. However, how to evaluate the customers’ credit is a complicated problem. In this paper, we present a Customer Credit Visual Analysis Model (CCVAM) that can be used to evaluate and classify the credibility of new customers according to the historical data about past customers. The model is based on Markov Logic Networks (MLNs) that combines probability and first-order logic with a weight attached to each formula. In this model, the basic rules or indexes based on expert knowledge a

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25

Rabehasaina, Landy, and Bruno Sericola. "A second-order Markov-modulated fluid queue with linear service rate." Journal of Applied Probability 41, no. 03 (2004): 758–77. http://dx.doi.org/10.1017/s0021900200020532.

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We consider an infinite-capacity second-order fluid queue governed by a continuous-time Markov chain and with linear service rate. The variability of the traffic is modeled by a Brownian motion and a local variance function modulated by the Markov chain and proportional to the fluid level in the queue. The behavior of this second-order fluid-flow model is described by a linear stochastic differential equation, satisfied by the transient queue level. We study the transient level's convergence in distribution under weak assumptions and we obtain an expression for the stationary queue level. For

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Rabehasaina, Landy, and Bruno Sericola. "A second-order Markov-modulated fluid queue with linear service rate." Journal of Applied Probability 41, no. 3 (2004): 758–77. http://dx.doi.org/10.1239/jap/1091543424.

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We consider an infinite-capacity second-order fluid queue governed by a continuous-time Markov chain and with linear service rate. The variability of the traffic is modeled by a Brownian motion and a local variance function modulated by the Markov chain and proportional to the fluid level in the queue. The behavior of this second-order fluid-flow model is described by a linear stochastic differential equation, satisfied by the transient queue level. We study the transient level's convergence in distribution under weak assumptions and we obtain an expression for the stationary queue level. For

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27

Yang, Jiali, Kun Lang, Guangle Zhang, Xiaodan Fan, Yuanyuan Chen, and Cong Pian. "SOMM4mC: a second-order Markov model for DNA N4-methylcytosine site prediction in six species." Bioinformatics 36, no. 14 (2020): 4103–5. http://dx.doi.org/10.1093/bioinformatics/btaa507.

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Abstract Motivation DNA N4-methylcytosine (4mC) modification is an important epigenetic modification in prokaryotic DNA due to its role in regulating DNA replication and protecting the host DNA against degradation. An efficient algorithm to identify 4mC sites is needed for downstream analyses. Results In this study, we propose a new prediction method named SOMM4mC based on a second-order Markov model, which makes use of the transition probability between adjacent nucleotides to identify 4mC sites. The results show that the first-order and second-order Markov model are superior to the three exi

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Wellmann, J. "Markov Models for Repeated Ordinal Data." Methods of Information in Medicine 45, no. 04 (2006): 414–18. http://dx.doi.org/10.1055/s-0038-1634097.

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Summary Objectives: To demonstrate the application of Markov models, especially for ordinal outcomes, within the context of regression models for correlated data. Methods: A brief review of regression methods for correlated data is given. A proportional odds model and a continuation ratio model is applied to repeated measurements of macular pigment density, obtained in an intervention study on the supplementation of macular carotenoids. The correlation between repeated assessments is assumed to follow a first-order Markov model. The models are implemented with standard statistical software. Re

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Zhu, G. George. "Weighted multirate q-Markov Cover identification using PRBS – an application to engine systems." Mathematical Problems in Engineering 6, no. 2-3 (2000): 201–24. http://dx.doi.org/10.1155/s1024123x00001332.

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The q-Markov COVariance Equivalent Realization (q-Markov Cover) method for identification uses either pulse, white noise or PRBS (Pseudo-Random Binary Signal) as test excitation. This paper extended the q-Markov Cover using PRBS to the weighted multirate case, that is, the sample rate of the PRBS signal is different from the system output one. Then, the multirate PRBS q-Markov Cover is applied to identify a diesel engine model from the fuel command input to the engine speed output. The identified engine model has order of two and approximates the pure fuel system time delay using a first-order

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Chatzis, Sotirios, Dimitrios Kosmopoulos, and George Papadourakis. "A Nonstationary Hidden Markov Model with Approximately Infinitely-Long Time-Dependencies." International Journal on Artificial Intelligence Tools 25, no. 05 (2016): 1640001. http://dx.doi.org/10.1142/s0218213016400017.

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Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first-order Markov chain. In other words, only one-step back dependencies are modeled which is a rather unrealistic assumption in most applications. In this paper, we propose a method for postulating HMMs with approximately infinitely-long time-dependencies. Our approach considers the whole history of model states in the postulated dependencies, by making use of a recently proposed nonparametric Bayesian method for modeling label sequences with infinitely-long time dependenci

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Deka, Surobhi. "Determination of the Order of a Markov Chain for Daily Rainfall Data of North East India: "Application AIC criterion"." Journal of Applied and Natural Science 10, no. 1 (2018): 80–87. http://dx.doi.org/10.31018/jans.v10i1.1583.

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The paper aims at demonstrating the application of the Akaike information criterion to determine the order of two state Markov chain for studying the pattern of occurrence of wet and dry days during the rainy season (April to September) in North-East India. For each station, each day is classified as dry day if the amount of rainfall is less than 3 mm and wet day if the amount of rainfall is greater than or equal to 3 mm. We apply Markov chain of order up to three to the sequences of wet and dry days observed at seven distantly located stations in North East region of India. The Markov chain m

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Lin, Thy-Hou, Ging-Ming Wang та Yen-Tseng Wang. "Prediction of β-Turns in Proteins Using the First-Order Markov Models". Journal of Chemical Information and Computer Sciences 42, № 1 (2001): 123–33. http://dx.doi.org/10.1021/ci0103020.

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Jin, Xue Bo, Jing Jing Du, and Jia Bao. "Data-Driven Tracking Based on Kalman Filter." Applied Mechanics and Materials 226-228 (November 2012): 2476–79. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.2476.

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A good model of the target will extract useful information about the target’s state from observations effectively. There are many models used to maneuvering target tracking, such as constant-velocity (CV) models, Singer acceleration model (zero-mean first-order Markov model) and “current” model (Mean-Adaptive Acceleration Model), etc. While due to the complexity of maneuvering target, to seek the target model which can get better performance is still a subject worthy of study. For the AR process, autocorrelation function is estimated by the random sampling points in this paper. We have the sta

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Fu, Xiao, Jiaxu Zhang, and Yue Zhang. "An Online Map Matching Algorithm Based on Second-Order Hidden Markov Model." Journal of Advanced Transportation 2021 (July 17, 2021): 1–12. http://dx.doi.org/10.1155/2021/9993860.

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Map matching is a key preprocess of trajectory data which recently have become a major data source for various transport applications and location-based services. In this paper, an online map matching algorithm based on the second-order hidden Markov model (HMM) is proposed for processing trajectory data in complex urban road networks such as parallel road segments and various road intersections. Several factors such as driver’s travel preference, network topology, road level, and vehicle heading are well considered. An extended Viterbi algorithm and a self-adaptive sliding window mechanism ar

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35

Nagata, Yuichi. "High-Order Entropy-Based Population Diversity Measures in the Traveling Salesman Problem." Evolutionary Computation 28, no. 4 (2020): 595–619. http://dx.doi.org/10.1162/evco_a_00268.

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To maintain the population diversity of genetic algorithms (GAs), we are required to employ an appropriate population diversity measure. However, commonly used population diversity measures designed for permutation problems do not consider the dependencies between the variables of the individuals in the population. We propose three types of population diversity measures that address high-order dependencies between the variables to investigate the effectiveness of considering high-order dependencies. The first is formulated as the entropy of the probability distribution of individuals estimated

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Roshan, Gholamreza, and Panagiotis T. Nastos. "Assessment of extreme heat stress probabilities in Iran's urban settlements, using first order Markov chain model." Sustainable Cities and Society 36 (January 2018): 302–10. http://dx.doi.org/10.1016/j.scs.2017.10.013.

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Maafi, A., and A. Adane. "Analysis of the performances of the first-order two-state Markov model using solar radiation properties." Renewable Energy 13, no. 2 (1998): 175–93. http://dx.doi.org/10.1016/s0960-1481(97)00094-3.

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Li, Chunhua, Pengpeng Zhao, Jian Wu, and Zhiming Cui. "Anatomy Ontology Matching Using Markov Logic Networks." Scientifica 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/1010946.

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The anatomy of model species is described in ontologies, which are used to standardize the annotations of experimental data, such as gene expression patterns. To compare such data between species, we need to establish relationships between ontologies describing different species. Ontology matching is a kind of solutions to find semantic correspondences between entities of different ontologies. Markov logic networks which unify probabilistic graphical model and first-order logic provide an excellent framework for ontology matching. We combine several different matching strategies through first-

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McLay, Justin G. "Markov Chain Modeling of Sequences of Lagged NWP Ensemble Probability Forecasts: An Exploration of Model Properties and Decision Support Applications." Monthly Weather Review 136, no. 10 (2008): 3655–70. http://dx.doi.org/10.1175/2008mwr2376.1.

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It is shown that sequences of lagged ensemble-derived probability forecasts can be treated as being realizations of a discrete, finite-step Markov chain. A reforecast ensemble dataset is used to explore this idea for the case in which the Markov chain has 12 states and 15 steps and the probability forecasts are for the event for which the 500-hPa geopotential height exceeds the climatological value at a specified point. Results suggest that the transition probabilities of the Markov chain are best modeled as first order if they are obtained from the reforecast ensemble dataset using maximum li

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40

Židanavičiutė, J. "LOGIT ANALYSIS OF GENETIC DATA." Mathematical Modelling and Analysis 13, no. 1 (2008): 135–44. http://dx.doi.org/10.3846/1392-6292.2008.13.135-144.

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A new framework of genetic sequence statistical analysis based on generalized logit model is introduced. Logit analysis is applied to assess the dependence structure (interactions) between DNA nucleotides and to test hypothesis about Markov order of these dependencies. The procedure proposed seeks the non‐coding subsequences which are homogeneous but yet non‐Markov. It has been shown, that even homogeneous DNA regions can not be treated as the first order Markov sequences.

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Sidorov, S. M. "Hidden Markov Model of Two-Component System with Group Instantly Replenished Time Reserve." INFORMACIONNYE TEHNOLOGII 27, no. 2 (2021): 64–71. http://dx.doi.org/10.17587/it.27.64-71.

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Most systems allow the construction of a semi-Markov model. However, during the operation of the system, full information contained in the state encoding is not always available, but it is possible to obtain some signal (information). Tasks arise to assess the consistency of the model with the received data (signals), to refine the model and its parameters. Such parameters can be characteristics of random values characterizing system operation, time reserve value, etc. The theory of hidden Markov models allows solving these problems. In order to move from a semi-Markov model of the system to i

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Assaf, David, Moshe Shared, and J. George shanthikumar. "First-passage times with PFr densities." Journal of Applied Probability 22, no. 1 (1985): 185–96. http://dx.doi.org/10.2307/3213758.

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It is shown that if a finite-state continuous-time Markov process can be uniformized such that the embedded Markov chain has a TPr (totally positive of order r) transition matrix, then the first-passage time from state 0 to any other state has a PFr (Polya frequency of order r) density. As a consequence, results of Keilson (1971), Esary, Marshall and Proschan (1973), Ghosh and Ebrahimi (1982) and Derman, Ross and Schechner (1983) are strengthened. It is also shown that some cumulative damage shock models, with an underlying compound Poisson process and ‘damages' which are not necessarily non-n

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Assaf, David, Moshe Shared, and J. George shanthikumar. "First-passage times with PFr densities." Journal of Applied Probability 22, no. 01 (1985): 185–96. http://dx.doi.org/10.1017/s0021900200029119.

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It is shown that if a finite-state continuous-time Markov process can be uniformized such that the embedded Markov chain has a TPr (totally positive of order r) transition matrix, then the first-passage time from state 0 to any other state has a PF r (Polya frequency of order r) density. As a consequence, results of Keilson (1971), Esary, Marshall and Proschan (1973), Ghosh and Ebrahimi (1982) and Derman, Ross and Schechner (1983) are strengthened. It is also shown that some cumulative damage shock models, with an underlying compound Poisson process and ‘damages' which are not necessarily non-

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Norberg, Ragnar. "The Markov Chain Market." ASTIN Bulletin 33, no. 02 (2003): 265–87. http://dx.doi.org/10.2143/ast.33.2.503693.

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We consider a financial market driven by a continuous time homogeneous Markov chain. Conditions for absence of arbitrage and for completeness are spelled out, non-arbitrage pricing of derivatives is discussed, and details are worked out for some cases. Closed form expressions are obtained for interest rate derivatives. Computations typically amount to solving a set of first order partial differential equations. An excursion into risk minimization in the incomplete case illustrates the matrix techniques that are instrumental in the model.

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Norberg, Ragnar. "The Markov Chain Market." ASTIN Bulletin 33, no. 2 (2003): 265–87. http://dx.doi.org/10.1017/s0515036100013465.

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We consider a financial market driven by a continuous time homogeneous Markov chain. Conditions for absence of arbitrage and for completeness are spelled out, non-arbitrage pricing of derivatives is discussed, and details are worked out for some cases. Closed form expressions are obtained for interest rate derivatives. Computations typically amount to solving a set of first order partial differential equations. An excursion into risk minimization in the incomplete case illustrates the matrix techniques that are instrumental in the model.

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Ouyang, Chun-Juan, Ming Leng, Jie-Wu Xia, and Huan Liu. "Vague Sets Security Measure for Steganographic System Based on High-Order Markov Model." Security and Communication Networks 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/1790268.

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Security measure is of great importance in both steganography and steganalysis. Considering that statistical feature perturbations caused by steganography in an image are always nondeterministic and that an image is considered nonstationary, in this paper, the steganography is regarded as a fuzzy process. Here a steganographic security measure is proposed. This security measure evaluates the similarity between two vague sets of cover images and stego images in terms of n-order Markov chain to capture the interpixel correlation. The new security measure has proven to have the properties of boun

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Tamazin, M., A. Noureldin, and M. J. Korenberg. "Robust Modeling of Low-Cost MEMS Sensor Errors in Mobile Devices Using Fast Orthogonal Search." Journal of Sensors 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/101820.

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Accessibility to inertial navigation systems (INS) has been severely limited by cost in the past. The introduction of low-cost microelectromechanical system-based INS to be integrated with GPS in order to provide a reliable positioning solution has provided more wide spread use in mobile devices. The random errors of the MEMS inertial sensors may deteriorate the overall system accuracy in mobile devices. These errors are modeled stochastically and are included in the error model of the estimated techniques used such as Kalman filter or Particle filter. First-order Gauss-Markov model is usually

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Zhipeng, Jiang, and Huang Chengwei. "High-Order Markov Random Fields and Their Applications in Cross-Language Speech Recognition." Cybernetics and Information Technologies 15, no. 4 (2015): 50–57. http://dx.doi.org/10.1515/cait-2015-0054.

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Abstract In this paper we study the cross-language speech emotion recognition using high-order Markov random fields, especially the application in Vietnamese speech emotion recognition. First, we extract the basic speech features including pitch frequency, formant frequency and short-term intensity. Based on the low level descriptor we further construct the statistic features including maximum, minimum, mean and standard deviation. Second, we adopt the high-order Markov random fields (MRF) to optimize the cross-language speech emotion model. The dimensional restrictions may be modeled by MRF.

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Chi, Cheng Ying, and Yan Zhang. "Information Extraction from Chinese Papers Based on Hidden Markov Model." Advanced Materials Research 846-847 (November 2013): 1291–94. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1291.

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Hidden Markov model HMM (1) is one of the important approaches for information extraction. In this paper, a model of the improved first-order hidden Markov HMM (2) is proposed. In the HMM (2), the output probability of the observation is not only dependent on the current state of the model, but also dependent on the previous state of the current state of the model. The algorithm of the ML and the algorithm of the Viterbi are analyzed. At last, experiments show that the HMM (2) is more precise than the HMM (1).

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MUNI TOKE, IOANE. "STATIONARY DISTRIBUTION OF THE VOLUME AT THE BEST QUOTE IN A POISSON ORDER BOOK MODEL." International Journal of Theoretical and Applied Finance 20, no. 06 (2017): 1750039. http://dx.doi.org/10.1142/s021902491750039x.

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We develop a Markovian model that deals with the volume offered at the best quote of an electronic order book. The volume of the first limit is a stochastic process whose paths are periodically interrupted and reset to a new value, either by a new limit order submitted inside the spread or by a market order that removes the first limit. Using applied probability results on killing and resurrecting Markov processes, we derive the stationary distribution of the volume offered at the best quote. All proposed models are empirically fitted and compared, stressing the importance of the proposed mech

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