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Journal articles on the topic 'Variable Order Markov Model'

Author: Grafiati

Published: 9 March 2023

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1

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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Begleiter, R., R. El-Yaniv, and G. Yona. "On Prediction Using Variable Order Markov Models." Journal of Artificial Intelligence Research 22 (December 1, 2004): 385–421. http://dx.doi.org/10.1613/jair.1491.

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This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Context Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic Suffix Trees (PSTs). We discuss the properties of these algorithms and compare their performance using real life sequences from three domains: proteins, English text and music pieces. The comparison is made with respect to

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Tengke Xiong, Shengrui Wang, Qingshan Jiang, and Joshua Zhexue Huang. "A Novel Variable-order Markov Model for Clustering Categorical Sequences." IEEE Transactions on Knowledge and Data Engineering 26, no. 10 (2014): 2339–53. http://dx.doi.org/10.1109/tkde.2013.104.

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Cunial, Fabio, Jarno Alanko, and Djamal Belazzougui. "A framework for space-efficient variable-order Markov models." Bioinformatics 35, no. 22 (2019): 4607–16. http://dx.doi.org/10.1093/bioinformatics/btz268.

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Abstract Motivation Markov models with contexts of variable length are widely used in bioinformatics for representing sets of sequences with similar biological properties. When models contain many long contexts, existing implementations are either unable to handle genome-scale training datasets within typical memory budgets, or they are optimized for specific model variants and are thus inflexible. Results We provide practical, versatile representations of variable-order Markov models and of interpolated Markov models, that support a large number of context-selection criteria, scoring function

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POSCH, STEFAN, JAN GRAU, ANDRE GOHR, IRAD BEN-GAL, ALEXANDER E. KEL, and IVO GROSSE. "RECOGNITION OF CIS-REGULATORY ELEMENTS WITH VOMBAT." Journal of Bioinformatics and Computational Biology 05, no. 02b (2007): 561–77. http://dx.doi.org/10.1142/s0219720007002886.

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Variable order Markov models and variable order Bayesian trees have been proposed for the recognition of cis-regulatory elements, and it has been demonstrated that they outperform traditional models such as position weight matrices, Markov models, and Bayesian trees for the recognition of binding sites in prokaryotes. Here, we study to which degree variable order models can improve the recognition of eukaryotic cis-regulatory elements. We find that variable order models can improve the recognition of binding sites of all the studied transcription factors. To ease a systematic evaluation of dif

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Xiao Li, Yuhao Wang, and Yuan Liu. "A Channel Cognitive Method for Local Fading Characteristics using Variable-Order Markov Model." Journal of Communications and Information Sciences 1, no. 2 (2011): 1–12. http://dx.doi.org/10.4156/jcis.vol1.issue2.1.

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Saadani, A., P. Gelpi, and P. Tortelier. "A Variable-Order Markov-Chain-Based Model for Rayleigh Fading and Rake Receiver." IEEE Signal Processing Letters 11, no. 3 (2004): 356–58. http://dx.doi.org/10.1109/lsp.2003.822915.

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Qi, Zhang, Wen Guang, Chen Zhixin, et al. "Contact stress reliability analysis based on first order second moment for variable hyperbolic circular arc gear." Advances in Mechanical Engineering 14, no. 7 (2022): 168781322211112. http://dx.doi.org/10.1177/16878132221111210.

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Aiming at the contact strength reliability of variable hyperbolic circular arc gear, a reliability analysis method for contact strength of variable hyperbolic circular arc gear based on Kriging model and advanced first-order and second-moment algorithm is proposed. Kriging model was used to establish the limit state equation of the contact stress reliability analysis of variable hyperbolic circular arc gear, and the advanced first-order second-moment method was used to analyze the contact stress reliability of variable hyperbolic circular arc gear based on the limit state equation of the conta

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Melikov, A. Z., L. A. Ponomarenko, and S. A. Bagirova. "Markov Models of Queueing–Inventory Systems with Variable Order Size." Cybernetics and Systems Analysis 53, no. 3 (2017): 373–86. http://dx.doi.org/10.1007/s10559-017-9937-3.

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Kohli, Amit Kumar, Amrita Rai, and Meher Krishna Patel. "Variable Forgetting Factor LS Algorithm for Polynomial Channel Model." ISRN Signal Processing 2011 (December 30, 2011): 1–4. http://dx.doi.org/10.5402/2011/915259.

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Variable forgetting factor (VFF) least squares (LS) algorithm for polynomial channel paradigm is presented for improved tracking performance under nonstationary environment. The main focus is on updating VFF when each time-varying fading channel is considered to be a first-order Markov process. In addition to efficient tracking under frequency-selective fading channels, the incorporation of proposed numeric variable forgetting factor (NVFF) in LS algorithm reduces the computational complexity.

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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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Chen, Juan. "A Novel Variable Order Markov Model Based Wireless Service Prediction Algorithm with User Similarity." Journal of Information and Computational Science 12, no. 17 (2015): 6267–76. http://dx.doi.org/10.12733/jics20106976.

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Wu, Shih-Lin, Jen-Jee Chen, and Wen-Chiang Chou. "Cell-related location area planning for 4G PCS networks with variable-order Markov model." Journal of Systems and Software 86, no. 10 (2013): 2688–99. http://dx.doi.org/10.1016/j.jss.2013.05.031.

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Al-Shatnawi, Mufleh, M. Omair Ahmad, and M. N. S. Swamy. "Prediction of Indel flanking regions in protein sequences using a variable-order Markov model." Bioinformatics 31, no. 1 (2014): 40–47. http://dx.doi.org/10.1093/bioinformatics/btu556.

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Apsel, Udi, and Ronen Brafman. "Lifted MEU by Weighted Model Counting." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1861–67. http://dx.doi.org/10.1609/aaai.v26i1.8396.

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Recent work in the field of probabilistic inference demonstrated the efficiency of weighted model counting (WMC) enginesfor exact inference in propositional and, very recently, first order models. To date, these methods have not been applied to decision making models, propositional or first order, such as influence diagrams, and Markov decision networks (MDN). In this paper we show how this technique can be applied to such models. First, we show how WMC can be used to solve (propositional) MDNs. Then, we show how this can be extended to handle a first-order model — the Markov Logic Decision Ne

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Chen, Haiying, Haiyan Chen, Wei Zhang, Chaodan Yang, and Hongxiu Cui. "Research on Marketing Prediction Model Based on Markov Prediction." Wireless Communications and Mobile Computing 2021 (December 15, 2021): 1–9. http://dx.doi.org/10.1155/2021/4535181.

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Many activities in modern business marketing management are random and repetitive. The marketing effect is constantly influenced by a variety of factors such as changing market supply and demand, customers’ purchase intentions, and national financial policy. As a result, Markov analysis can be used to analyze the status and trend of some variables, that is, to predict the future status and trend of a variable based on its current status and trend, in order to forecast possible changes in the future and take appropriate countermeasures. The mathematical model of product marketing prediction is

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Meng, Jingxiang. "Comparison of Statistical Estimators for Estimating the Orders of Markov Chains." Journal of Physics: Conference Series 2386, no. 1 (2022): 012004. http://dx.doi.org/10.1088/1742-6596/2386/1/012004.

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Abstract High order discrete Markov chain is essential to analyze the dependency structure of data sets. To apply Markov chain correctly, even though the true order is an unknown parameter, statisticians have developed multiple order estimators. It is natural to identify the strongest order estimators under different parameter combinations. Aim for evaluating the performance of estimators, we study four of them in this paper: Akaike information criteria (AIC), Bayesian information criteria (BIC), Maximal fluctuation estimation method (PS), and approximate χ 2 − distribution method (Dk ). We si

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18

Herkenrath, Ulrich. "On the uniform ergodicity of Markov processes of order 2." Journal of Applied Probability 40, no. 2 (2003): 455–72. http://dx.doi.org/10.1239/jap/1053003556.

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We study the uniform ergodicity of Markov processes (Zn, n ≥ 1) of order 2 with a general state space (Z, 𝒵). Markov processes of order higher than 1 were defined in the literature long ago, but scarcely treated in detail. We take as the basis for our considerations the natural transition probability Q of such a process. A Markov process of order 2 is transformed into one of order 1 by combining two consecutive variables Z2n–1 and Z2n into one variable Yn with values in the Cartesian product space (Z × Z, 𝒵 ⊗ 𝒵). Thus, a Markov process (Yn, n ≥ 1) of order 1 with transition probability R is ge

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Herkenrath, Ulrich. "On the uniform ergodicity of Markov processes of order 2." Journal of Applied Probability 40, no. 02 (2003): 455–72. http://dx.doi.org/10.1017/s0021900200019422.

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We study the uniform ergodicity of Markov processes (Z n , n ≥ 1) of order 2 with a general state space (Z, 𝒵). Markov processes of order higher than 1 were defined in the literature long ago, but scarcely treated in detail. We take as the basis for our considerations the natural transition probability Q of such a process. A Markov process of order 2 is transformed into one of order 1 by combining two consecutive variables Z 2n–1 and Z 2n into one variable Y n with values in the Cartesian product space (Z × Z, 𝒵 ⊗ 𝒵). Thus, a Markov process (Y n , n ≥ 1) of order 1 with transition probability

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Wang, Xing, Xinhua Jiang, Lifei Chen, and Yi Wu. "KVLMM: A Trajectory Prediction Method Based on a Variable-Order Markov Model With Kernel Smoothing." IEEE Access 6 (2018): 25200–25208. http://dx.doi.org/10.1109/access.2018.2829545.

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Guseva, Maria, and Andrey Silaev. "Applying Bayesian methods for macroeconomic modeling of business cycle phases." St Petersburg University Journal of Economic Studies 37, no. 2 (2021): 298–317. http://dx.doi.org/10.21638/spbu05.2021.205.

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In the present research, the features of applying two models for estimating macroeconomic dynamic in the USA are investigated: Bayesian vector autoregression and Bayesian vector autoregression with Markov switching. The research goal is to identify periods, structure of fluctuations and the main directions of interaction of the variables (real US GDP and employment) using Bayesian vector autoregression models. Models with Markov chains include many equations (structures). The switching mechanisms between these structures are controlled by an unobservable variable that follows a first-order Mar

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Kasse, Irwan, Didiharyono Didiharyono, and Maulidina Maulidina. "Metode Markov Chain untuk Menghitung Premi Asuransi pada Pasien Penderita Penyakit Demam Berdarah Dengue." Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam 7, no. 2 (2020): 151–60. http://dx.doi.org/10.24256/jpmipa.v7i2.1251.

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Abstract:This paper discusses the Markov Chain method in calculating insurance premiums for patients with dengue hemorrhagic fever (DHF) at Labuang Baji Hospital. The Markov Chain Model is a method that studies the characteristics of a variable in the present that depends on its properties in the past in an attempt to estimate the properties of these variables in the future. This paper aims to determine the transition probability model for each circumstance using the Markov multistate model and to determine insurance premiums using the Markov Method. Based on the results of research and discus

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Xia, Ying, Yu Gong, Xu Zhang, and Hae-young Bae. "Location Prediction based on Variable-order Markov Model with Time Feature and User’s Spatio-temporal Rule." Advances in Science, Technology and Engineering Systems Journal 4, no. 2 (2019): 351–56. http://dx.doi.org/10.25046/aj040244.

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BORGES, JOSÉ, and MARK LEVENE. "A COMPARISON OF SCORING METRICS FOR PREDICTING THE NEXT NAVIGATION STEP WITH MARKOV MODEL-BASED SYSTEMS." International Journal of Information Technology & Decision Making 09, no. 04 (2010): 547–73. http://dx.doi.org/10.1142/s0219622010003956.

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The problem of predicting the next request during a user's navigation session has been extensively studied. In this context, higher-order Markov models have been widely used to model navigation sessions and to predict the next navigation step, while prediction accuracy has been mainly evaluated with the hit and miss score. We claim that this score, although useful, is not sufficient for evaluating next link prediction models with the aim of finding a sufficient order of the model, the size of a recommendation set, and assessing the impact of unexpected events on the prediction accuracy. Herein

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Zhu, Yunsheng, Jinxu Chen, Kaifeng Wang, Yong Liu, and Yanting Wang. "Research on Performance Prediction of Highway Asphalt Pavement Based on Grey–Markov Model." Transportation Research Record: Journal of the Transportation Research Board 2676, no. 4 (2021): 194–209. http://dx.doi.org/10.1177/03611981211057527.

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Reasonable and accurate forecasts can be used by the highway maintenance management department to determine the best maintenance timing and strategy, which can keep the highway performing well and maximize its social and economic benefits. A Grey–Markov combination model is established in this paper to predict highway pavement performance accurately based on the Grey GM (1, 1) model (a single-variable Grey prediction model with a first-order difference equation) and revised by the Markov model. The advantages of the short-term forecast Grey model and the probabilistic Markov model, which consi

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Zhang, Weibin, Yong Qi, Zhuping Zhou, Salvatore A. Biancardo, and Yinhai Wang. "Method of speed data fusion based on Bayesian combination algorithm and high-order multi-variable Markov model." IET Intelligent Transport Systems 12, no. 10 (2018): 1312–21. http://dx.doi.org/10.1049/iet-its.2018.5020.

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27

Berchtold, André. "The Predictive Power of Transition Matrices." Symmetry 13, no. 11 (2021): 2096. http://dx.doi.org/10.3390/sym13112096.

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When working with Markov chains, especially if they are of order greater than one, it is often necessary to evaluate the respective contribution of each lag of the variable under study on the present. This is particularly true when using the Mixture Transition Distribution model to approximate the true fully parameterized Markov chain. Even if it is possible to evaluate each transition matrix using a standard association measure, these measures do not allow taking into account all the available information. Therefore, in this paper, we introduce a new class of so-called "predictive power" meas

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Paluszkiewicz, Theresa, and Charles F. Marshall. "COMPARISON OF TECHNIQUES FOR FORCING AN OIL SPILL TRAJECTORY MODEL." International Oil Spill Conference Proceedings 1989, no. 1 (1989): 547–53. http://dx.doi.org/10.7901/2169-3358-1989-1-547.

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ABSTRACT The Oil Spill Risk Analysis model used by the U.S. Department of the Interior simulates trajectories of hypothetical oil spills on the outer continental shelf. The trajectories are calculated from surface current fields provided by a general circulation model with a superimposed wind-drift simulation. This paper compares two techniques for driving the wind-drift portion of the trajectory model. In this comparison, a statistical model of the wind, utilizing a first-order Markov approach, is used to simulate wind records to drive the model; these results are compared with trajectories f

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Zhao, Kun, Hongwei Ding, Kai Ye, and Xiaohui Cui. "A Transformer-Based Hierarchical Variational AutoEncoder Combined Hidden Markov Model for Long Text Generation." Entropy 23, no. 10 (2021): 1277. http://dx.doi.org/10.3390/e23101277.

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The Variational AutoEncoder (VAE) has made significant progress in text generation, but it focused on short text (always a sentence). Long texts consist of multiple sentences. There is a particular relationship between each sentence, especially between the latent variables that control the generation of the sentences. The relationships between these latent variables help in generating continuous and logically connected long texts. There exist very few studies on the relationships between these latent variables. We proposed a method for combining the Transformer-Based Hierarchical Variational A

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Li, RuiChang. "Joint Modeling of User Behaviors Based on Variable-Order Additive Markov Chain for POI Recommendation." Wireless Communications and Mobile Computing 2021 (November 23, 2021): 1–13. http://dx.doi.org/10.1155/2021/4359369.

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The POI recommendation system has become an important means to help people discover attractive and interesting places. Based on our data analysis, we observe that users pay equal attention to conservatism and curiosity. In particular, adopting analysis corresponding to different time intervals, we find that users lean towards old POIs in the short term and look for new POIs with the increase of the time interval. However, existing approaches usually neglect users’ conservatism and curiosity preferences. Therefore, they are confronted with a bottleneck of depicting accurate user needs, making i

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Haviv, Avery. "Technical Note—Cyclic Variables and Markov Decision Processes." Operations Research 68, no. 4 (2020): 1231–37. http://dx.doi.org/10.1287/opre.2019.1913.

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Markov decision processes are commonly used to model forward-looking behavior. However, cyclic terms, including seasonality, are often omitted from these models because of the increase in computational burden. This paper develops a cyclic value function iteration (CVFI), an adjustment to the standard value function iteration. By updating states in a specific order, CVFI allows cyclic variables to be included in the state space with no increase in the computational cost. This result is proved theoretically and shown to hold closely in Monte Carlo simulations.

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Liu, Zhenpeng, Dewei Miao, Ruilin Li, Yi Liu, and Xiaofei Li. "Cache-Based Privacy Protection Scheme for Continuous Location Query." Entropy 25, no. 2 (2023): 201. http://dx.doi.org/10.3390/e25020201.

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Users who initiate continuous location queries are prone to trajectory information leakage, and the obtained query information is not effectively utilized. To address these problems, we propose a continuous location query protection scheme based on caching and an adaptive variable-order Markov model. When a user initiates a query request, we first query the cache information to obtain the required data. When the local cache cannot satisfy the user’s demand, we use a variable-order Markov model to predict the user’s future query location and generate a k-anonymous set based on the predicted loc

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Barska, Magdalena. "Analysis of demand in steel and iron industry – latent variables model." Przegląd Statystyczny 66, no. 4 (2020): 247–69. http://dx.doi.org/10.5604/01.3001.0014.0951.

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Demand in the steel and iron industry is influenced by multiple factors. Not all of them can be identified and measured. The paper presents the results of the analysis of the levels of demand achieved by a selected enterprise from this sector in the years 2010–2014. The aim of the study is to build a hidden Markov model which would reflect the turning points of this demand, thus making it possible to forecast its future levels. The model’s forecasting properties and stability have been examined. A simulation has been carried out that involved generating a high number of series for selected mod

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Dalevi, D., D. Dubhashi, and M. Hermansson. "Bayesian classifiers for detecting HGT using fixed and variable order markov models of genomic signatures." Bioinformatics 22, no. 5 (2006): 517–22. http://dx.doi.org/10.1093/bioinformatics/btk029.

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Mardiati, Rina, Bambang R Trilaksono, Yudi S Gondokaryono, and Sony S Wibowo. "Motorcycle Movement Model Based on Markov Chain Process in Mixed Traffic." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 3149. http://dx.doi.org/10.11591/ijece.v8i5.pp3149-3157.

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<p>Mixed traffic systems are dynamically complex since there are many parameters and variables that influence the interactions between the different kinds of vehicles. Modeling the behavior of vehicles, especially motorcycle which has erratic behavior is still being developed continuously, especially in developing countries which have heterogeneous traffic. To get a better understanding of motorcycle behavior, one can look at maneuvers performed by drivers. In this research, we tried to build a model of motorcycle movement which only focused on maneuver action to avoid the obstacle along

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Ramesh, Nadarajah I., Gayatri Rode, and Christian Onof. "A Cox Process with State-Dependent Exponential Pulses to Model Rainfall." Water Resources Management 36, no. 1 (2021): 297–313. http://dx.doi.org/10.1007/s11269-021-03028-6.

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AbstractA point process model based on a class of Cox processes is developed to analyse precipitation data at a point location. The model is constructed using state-dependent exponential pulses that are governed by an unobserved underlying Markov chain. The mathematical formulation of the model where both the arrival rate of the rain cells and the initial pulse depth are determined by the Markov chain is presented. Second-order properties of the rainfall depth process are derived and utilised in model assessment. A method of moment estimation is employed in model fitting. The proposed model is

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Wang, Yuanhong, Timothy van Bremen, Yuyi Wang, and Ondřej Kuželka. "Domain-Lifted Sampling for Universal Two-Variable Logic and Extensions." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (2022): 10070–79. http://dx.doi.org/10.1609/aaai.v36i9.21246.

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Given a first-order sentence ? and a domain size n, how can one sample a model of ? on the domain {1, . . . , n} efficiently as n scales? We consider two variants of this problem: the uniform sampling regime, in which the goal is to sample a model uniformly at random, and the symmetric weighted sampling regime, in which models are weighted according to the number of groundings of each predicate appearing in them. Solutions to this problem have applications to the scalable generation of combinatorial structures, as well as sampling in several statistical-relational models such as Markov logic n

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Friedmann, Roberto, and Richard Fox. "On the Internal Organization of Consumers' Cognitive Schemata." Psychological Reports 65, no. 1 (1989): 115–26. http://dx.doi.org/10.2466/pr0.1989.65.1.115.

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An operational definition for internal-based versus external-based schema variables via the tangible versus intangible dichotomy is provided. Using strings of one-word associations made in response to a verbal stimulus, the stochastic structure associated with the use of these variables is investigated. Analysis shows that a first-order Markov chain model which allows for dependence between two consecutive schema variables is more appropriate than a Bernoulli model in the description of the internal organization of cognitive schemata. The phenomena of “chunking” and tangible versus intangible

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Tong, You Cheng, Yang Zhang, and Jun Zhou Yao. "Texture Segmentation of Jacquard Fabric Image Based on Multiresolution Markov Random Field." Applied Mechanics and Materials 101-102 (September 2011): 496–99. http://dx.doi.org/10.4028/www.scientific.net/amm.101-102.496.

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In order to develop an automated segmentation system for jacquard fabric images, a new approach based on MRMRF algorithm with variable weighing parameter is proposed in this paper. Firstly the variable weighting parameter different to the one in traditional MRMRF is described, which can provide a more accurate vector. The next step is MAP estimation and the model for texture segmentation. During this iterative process the initial value is big enough to learn more accurate parameters of feature energy. With the iterative number going on, the value will decrease and stop decreasing when the iter

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Wanke, Peter, Otávio Henrique dos Santos Figueiredo, and Jorge Junio Moreira Antunes. "Unveiling endogeneity and temporal dependence between tourism revenues/expenditures and macroeconomic variables in Brazil: A stochastic hidden Markov model approach." Tourism Economics 25, no. 1 (2018): 3–21. http://dx.doi.org/10.1177/1354816618787578.

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Thus far, a comprehensive analysis on the feedback processes that may exist between macroeconomic variables and tourism activity in Brazil is still missing. This article fills this literature gap by analyzing the endogenous and temporally dependent pattern between Brazilian monthly tourism revenue/expenditures and macroeconomic variables over 20 years. A novel stochastic hidden Markov model approach reveals the feedback processes that exist between them. While tourism revenues are autocorrelated and impacted by gross domestic product (GDP) growth, tourism expenditures are detached from any mac

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Wang, Xi Jie, and Xiao Fan Zhao. "Texture Image Segmentation Based on MRMRF in Contourlet Domain." Advanced Materials Research 532-533 (June 2012): 732–37. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.732.

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This paper presents a new multi-resolution Markov random field model in Contourlet domain for unsupervised texture image segmentation. In order to make full use of the merits of Contourlet transformation, we introduce the taditional MRMRF model into Contourlet domain, in a manner of variable interation between two components in the tradtional MRMRF model. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmenta

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Muhirwa, J. P., S. I. Mbalawata, and V. G. Masanja. "Markov Chain Monte Carlo Analysis of the Variable-Volume Exothermic Model for a Continuously Stirred Tank Reactor." Engineering, Technology & Applied Science Research 11, no. 2 (2021): 6919–29. http://dx.doi.org/10.48084/etasr.3962.

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In this paper, a variable-volume Continuously Stirred Tank Reactor (CSTR) deterministic exothermic model has been formulated based on the Reynold Transport Theorem. The numerical analysis of the formulated model and the identifiability of its physical parameters are done by using the least squares and the Delayed-Rejection Adaptive Metropolis (DRAM) method. The least square estimates provide the prior information for the DRAM method. The overall numerical results show that the model gives an insight in describing the dynamics of CSTR processes, and 14 parameters of the CSTR are well identified

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FRANK, T. D. "COLLECTIVE BEHAVIOR OF BIOPHYSICAL SYSTEMS WITH THERMODYNAMIC FEEDBACK LOOPS: A CASE STUDY FOR A NONLINEAR MARKOV MODEL — THE TAKATSUJI SYSTEM." Modern Physics Letters B 25, no. 08 (2011): 551–68. http://dx.doi.org/10.1142/s0217984911025845.

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We study order–disorder transitions and the emergence of collective behavior using a particular mean field model: the dynamic Takatsuji system. This model satisfies linear non-equilibrium thermodynamics and can be described in terms of a nonlinear Markov process defined by a nonlinear Fokker–Planck equation, that is, an evolution equation that is nonlinear with respect to its probability density. We discuss quantitatively the impact of a feedback loop that involves a macroscopic, thermodynamic variable. We demonstrate by means of semi-analytical methods and numerical simulations that the feedb

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Nuijten, Mark J. C., and Pieter H. A. J. M. Van Gelder. "A Concise Equation That Captures the Essential Elements of One-Way Sensitivity Analyses in Health Economic Models." Medical Decision Making 31, no. 4 (2011): 642–49. http://dx.doi.org/10.1177/0272989x10393975.

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Objective: Sensitivity analyses are often performed on only a limited number of variables without justification of the choice of variables and range of each variable. External parties such as health authorities are increasingly requiring submission of the actual model, often in order to test the robustness of the outcomes of the model by performing additional sensitivity analyses. The objective of this work was to develop an alternative method to capture the critical issues of a sensitivity analysis in a health economic model, especially regarding the selection of variables and determining the

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Lamine, Benrais, and Baha Nadia. "Object-Based Scene Classification Modeled by Hidden Markov Models Architecture." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (2021): 1–30. http://dx.doi.org/10.4018/ijcini.20211001.oa6.

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Multiclass classification problems such as document classification, medical diagnosis or scene classification are very challenging to address due to similarities between mutual classes. The use of reliable tools is necessary to get good classification results. This paper addresses the scene classification problem using objects as attributes. The process of classification is modeled by a famous mathematical tool: The Hidden Markov Models. We introduce suitable relations that scale the parameters of the Hidden Markov Model into variables of scene classification. The construction of Hidden Markov

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Et.al, Dr R. Rooba. "Webpage Recommendation System Based on the Social Media Semantic Details of the Website." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 237–43. http://dx.doi.org/10.17762/turcomat.v12i6.1358.

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The web page recommendation is generated by using the navigational history from web server log files. Semantic Variable Length Markov Chain Model (SVLMC) is a web page recommendation system used to generate recommendation by combining a higher order Markov model with rich semantic data. The problem of state space complexity and time complexity in SVLMC was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) and Support vector machine based SVLCPMC (SSVLCPMC) meth-ods respectively. The recommendation accuracy was further improved by quickest change detection usin

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Wang, Weiwei, and Xiaoping Hu. "Pricing Israeli Option with Time-changed Compensation by an FFT-Based High-order Multinomial Tree in Lévy Markets." Computational Intelligence and Neuroscience 2022 (June 29, 2022): 1–8. http://dx.doi.org/10.1155/2022/9682292.

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The problem for pricing the Israel option with time-changed compensation was studied based on the high-order recombined multinomial tree by using a fast Fourier transform to approximate a Lévy process. First, the Lévy option pricing model and Fourier transform are introduced. Then, a network model based on FFT (Markov chain) is presented. After that, an FFT-based multinomial tree construction method is given to solve the problem of difficult parameter estimation when approximating the Lévy process with high-order multinomial trees. It is proved that the discrete random variables corresponding

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De Nijs, Roderick Sebastiaan, Christian Landsiedel, Dirk Wollherr, and Martin Buss. "Quadratization and Roof Duality of Markov Logic Networks." Journal of Artificial Intelligence Research 55 (March 25, 2016): 685–714. http://dx.doi.org/10.1613/jair.5023.

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This article discusses the quadratization of Markov Logic Networks, which enables efficient approximate MAP computation by means of maximum flows. The procedure relies on a pseudo-Boolean representation of the model, and allows handling models of any order. The employed pseudo-Boolean representation can be used to identify problems that are guaranteed to be solvable in low polynomial-time. Results on common benchmark problems show that the proposed approach finds optimal assignments for most variables in excellent computational time and approximate solutions that match the quality of ILP-based

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De Blasis, Riccardo, Giovanni Batista Masala, and Filippo Petroni. "A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm." Energies 14, no. 2 (2021): 388. http://dx.doi.org/10.3390/en14020388.

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The energy produced by a wind farm in a given location and its associated income depends both on the wind characteristics in that location—i.e., speed and direction—and the dynamics of the electricity spot price. Because of the evidence of cross-correlations between wind speed, direction and price series and their lagged series, we aim to assess the income of a hypothetical wind farm located in central Italy when all interactions are considered. To model these cross and auto-correlations efficiently, we apply a high-order multivariate Markov model which includes dependencies from each time ser

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Zufryden, Fred S. "Multibrand Transition Probabilities as a Function of Explanatory Variables: Estimation by a Least-Squares-Based Approach." Journal of Marketing Research 23, no. 2 (1986): 177–83. http://dx.doi.org/10.1177/002224378602300210.

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A model is formulated to express the relationship between first-order Markov transition probabilities for a multibrand market and explanatory variables. The author shows that the parameters of the model can be estimated through a proposed restricted weighted least squares procedure. An empirical implementation of the estimation procedure illustrates the structure, goodness of fit, and predictive validity of the proposed model.

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