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Journal articles on the topic 'Transition probability matrix'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Hasegawa, Shigeaki F., and Takenori Takada. "Probability of Deriving a Yearly Transition Probability Matrix for Land-Use Dynamics." Sustainability 11, no. 22 (2019): 6355. http://dx.doi.org/10.3390/su11226355.

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Takada’s group developed a method for estimating the yearly transition matrix by calculating the mth power roots of a transition matrix with an interval of m years. However, the probability of obtaining a yearly transition matrix with real and positive elements is unknown. In this study, empirical verification based on transition matrices from previous land-use studies and Monte-Carlo simulations were conducted to estimate the probability of obtaining an appropriate yearly transition probability matrix. In 62 transition probability matrices of previous land-use studies, 54 (87%) could provide

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2

Yuting Hu, Rong Xie, and Wenjun Zhang. "Transition Probability Matrix Based Tourists Flow Prediction." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 1 (2013): 194–201. http://dx.doi.org/10.4156/aiss.vol5.issue1.24.

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3

Kawabata, T., and K. Nishikawa. "Protein structure comparison using transition probability matrix." Seibutsu Butsuri 39, supplement (1999): S116. http://dx.doi.org/10.2142/biophys.39.s116_3.

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Eastman, J. Ronald, and Jiena He. "A Regression-Based Procedure for Markov Transition Probability Estimation in Land Change Modeling." Land 9, no. 11 (2020): 407. http://dx.doi.org/10.3390/land9110407.

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Land change models commonly model the expected quantity of change as a Markov chain. Markov transition probabilities can be estimated by tabulating the relative frequency of change for all transitions between two dates. To estimate the appropriate transition probability matrix for any future date requires the determination of an annualized matrix through eigendecomposition followed by matrix powering. However, the technique yields multiple solutions, commonly with imaginary parts and negative transitions, and possibly with no non-negative real stochastic matrix solution. In addition, the compu

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Riveros, Guillermo A., and Manuel E. Rosario-Pérez. "Deriving the transition probability matrix using computational mechanics." Engineering Computations 35, no. 2 (2018): 692–709. http://dx.doi.org/10.1108/ec-02-2017-0051.

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Purpose The combined effects of several complex phenomena cause the deterioration of elements in steel hydraulic structures (SHSs) within the US lock system: corrosion, cracking and fatigue, impact and overloads. Predicting the future condition state of these structures by the use of current condition state inspection data can be achieved through the probabilistic chain deterioration model. The purpose of this study is to derive the transition probability matrix using final elements modeling of a miter gate. Design/methodology/approach If predicted accurately, this information would yield bene

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Permana, D., U. S. Pasaribu, S. W. Indratno, and S. Suprayogi. "Convergence of Transition Probability Matrix in CLVMarkov Models." IOP Conference Series: Materials Science and Engineering 335 (April 2018): 012046. http://dx.doi.org/10.1088/1757-899x/335/1/012046.

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Yavin, Tzahi, Eugene Wang, Hu Zhang, and Michael A. Clayton. "Transition probability matrix methodology for incremental risk charge." Journal of Financial Engineering 01, no. 01 (2014): 1450010. http://dx.doi.org/10.1142/s234576861450010x.

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As part of Basel II's incremental risk charge (IRC) methodology, this paper summarizes our extensive investigations of constructing transition probability matrices (TPMs) for unsecuritized credit products in the trading book. The objective is to create monthly or quarterly TPMs with predefined sectors and ratings that are consistent with the bank's Basel PDs. Constructing a TPM is not a unique process. We highlight various aspects of three types of uncertainties embedded in different construction methods: (1) the available historical data and the bank's rating philosophy; (2) the merger of one

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F. Ahmed, Alaa. "A Proposed Bayesian Estimation of Transitional Probability for a Markov Chain with Random Times via Swarm Algorithm." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ) 56, no. 1 (2025): 411–20. https://doi.org/10.55562/jrucs.v56i1.37.

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The transition matrix estimators of the Markov chain are not accurate and the transition matrix is considered given. There are many methods that are used to estimate the transition probabilities matrix for different cases, the most famous of which is the Maximum Likelihood Method, in order to find a good and new estimator for the transition probabilities matrix of the Markov chain, a method was proposed, which is a modification of the Bayes method, to reach the transition probabilities with the least variance. This method assumes that the values of in the initial probability are estimated by t

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9

Wang, Xiaoxiao, and Yaotang Li. "On the Uniqueness of the Stationary Probability Matrix of Transition Probability Tensors." Frontiers of Mathematics 18, no. 6 (2023): 1421–45. http://dx.doi.org/10.1007/s11464-021-0165-9.

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10

Kyeong Lee, Jae, Mi Hwan Hyun, and Dong Gu Shin. "A study on website log data analysis methodology by transition probability." International Journal of Engineering & Technology 7, no. 2.12 (2018): 171. http://dx.doi.org/10.14419/ijet.v7i2.12.11118.

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Background/Objectives: To measure occupancy using transition probability matrix as a data analysis method to predict future requirements for web use. From this study, Executives facing business challenges can enhance the decision-making process for management and can be provided quantified evidence.Methods/Statistical analysis: Transition matrix and transition probability matrix are estimated if web users’ webpage use patterns are tied with frequency, using web log data. Occupancy is forecasted based on a Markov chain model.Findings: Data analysis from the perspective of web log-based marketin

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Jiang, Hao, Xi Chen, Yushan Qiu, and Wai-Ki Ching. "On Generating Optimal Sparse Probabilistic Boolean Networks with Maximum Entropy from a Positive Stationary Distribution." East Asian Journal on Applied Mathematics 2, no. 4 (2012): 353–72. http://dx.doi.org/10.4208/eajam.191012.221112a.

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Abstract.To understand a genetic regulatory network, two popular mathematical models, Boolean Networks (BNs) and its extension Probabilistic Boolean Networks (PBNs) have been proposed. Here we address the problem of constructing a sparse Probabilistic Boolean Network (PBN) from a prescribed positive stationary distribution. A sparse matrix is more preferable, as it is easier to study and identify the major components and extract the crucial information hidden in a biological network. The captured network construction problem is both ill-posed and computationally challenging. We present a novel

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12

Ren, Shuxia, Shubo Zhang, and Tao Wu. "An Improved Spectral Clustering Community Detection Algorithm Based on Probability Matrix." Discrete Dynamics in Nature and Society 2020 (June 4, 2020): 1–6. http://dx.doi.org/10.1155/2020/4540302.

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The similarity graphs of most spectral clustering algorithms carry lots of wrong community information. In this paper, we propose a probability matrix and a novel improved spectral clustering algorithm based on the probability matrix for community detection. First, the Markov chain is used to calculate the transition probability between nodes, and the probability matrix is constructed by the transition probability. Then, the similarity graph is constructed with the mean probability matrix. Finally, community detection is achieved by optimizing the NCut objective function. The proposed algorith

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13

Li, Pei-Sen, and Pan Zhao. "The Subdominant Eigenvalue of Möbius Monotone Transition Probability Matrix." Axioms 14, no. 7 (2025): 493. https://doi.org/10.3390/axioms14070493.

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We establish a Perron–Frobenius-type theorem for the subdominant eigenvalue of Möbius monotone transition matrices defined on partially ordered state spaces. This result extends the classical work of Keilson and Kester, where they considered stochastically monotone transition matrices in a totally ordered setting. Furthermore, we show that this subdominant eigenvalue is the geometric ergodicity rate.

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Zhang, Junhao, Kaicun Zhang, and Weiping Li. "An Improved Transition Probability Matrix for Crime Distribution Prediction." Computational Intelligence and Neuroscience 2022 (August 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/3925503.

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The occurrence of crime has always been the main problem affecting urban public security and social security environment. Therefore, the prevention and control of crime is the focus of public security work. The traditional police strategy has poor timeliness and cannot respond and adjust in real time with the occurrence of criminal activities, and its deterrence and control of criminal activities are limited. To address the problem of low accuracy of existing crime distribution prediction models, an improved transition probability matrix for crime distribution prediction is proposed in this pa

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15

Bidabad, Bijan, and Behrouz Bidabad. "Complex Probability and Markov Stochastic Process." Indian Journal of Finance and Banking 3, no. 1 (2019): 13–22. http://dx.doi.org/10.46281/ijfb.v3i1.290.

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This note discusses the existence of "complex probability" in the real world sensible problems. By defining a measure more general than the conventional definition of probability, the transition probability matrix of discrete Markov chain is broken to the periods shorter than a complete step of the transition. In this regard, the complex probability is implied.

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16

Wu, Bin, and Xiao Yi. "Research on the Combination Method of Temporal Conflict Evidence Based on Transition Matrix." Mathematical Problems in Engineering 2020 (April 2, 2020): 1–10. http://dx.doi.org/10.1155/2020/4370613.

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Conflict evidence combination is an important research topic in evidence theory. In this paper, two kinds of transition matrices are constructed based on the Markov model; one is the unordered transition matrix, which satisfies the commutative law, and the other is the temporal transition matrix, which does not satisfy the commutative law, but it can handle the combination of temporal evidence well. Then, a temporal conflict evidence combination model is proposed based on these two transition matrices. First, the transition probability at the first n time is calculated through the model of uno

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17

Guerry, Marie-Anne. "On the Embedding Problem for Discrete-Time Markov Chains." Journal of Applied Probability 50, no. 4 (2013): 918–30. http://dx.doi.org/10.1239/jap/1389370090.

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When a discrete-time homogenous Markov chain is observed at time intervals that correspond to its time unit, then the transition probabilities of the chain can be estimated using known maximum likelihood estimators. In this paper we consider a situation when a Markov chain is observed on time intervals with length equal to twice the time unit of the Markov chain. The issue then arises of characterizing probability matrices whose square root(s) are also probability matrices. This characterization is referred to in the literature as the embedding problem for discrete time Markov chains. The prob

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Guerry, Marie-Anne. "On the Embedding Problem for Discrete-Time Markov Chains." Journal of Applied Probability 50, no. 04 (2013): 918–30. http://dx.doi.org/10.1017/s002190020001370x.

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When a discrete-time homogenous Markov chain is observed at time intervals that correspond to its time unit, then the transition probabilities of the chain can be estimated using known maximum likelihood estimators. In this paper we consider a situation when a Markov chain is observed on time intervals with length equal to twice the time unit of the Markov chain. The issue then arises of characterizing probability matrices whose square root(s) are also probability matrices. This characterization is referred to in the literature as the embedding problem for discrete time Markov chains. The prob

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19

Ye, Dan, Quan-Yong Fan, Xin-Gang Zhao, and Guang-Hong Yang. "Stochastic Stability for Time-Delay Markovian Jump Systems with Sector-Bounded Nonlinearities and More General Transition Probabilities." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/208263.

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This paper is concerned with delay-dependent stochastic stability for time-delay Markovian jump systems (MJSs) with sector-bounded nonlinearities and more general transition probabilities. Different from the previous results where the transition probability matrix is completely known, a more general transition probability matrix is considered which includes completely known elements, boundary known elements, and completely unknown ones. In order to get less conservative criterion, the state and transition probability information is used as much as possible to construct the Lyapunov-Krasovskii

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20

Kalpazidou, S. "Rotational Representations of Transition Matrix Functions." Annals of Probability 22, no. 2 (1994): 703–12. http://dx.doi.org/10.1214/aop/1176988726.

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21

VOORHEES, BURTON. "FRACTAL DIMENSION, PROBABILITY AND ENTROPY IN MATRIX AND SUMMATION SUBSTITUTION SYSTEMS." Fractals 07, no. 03 (1999): 283–99. http://dx.doi.org/10.1142/s0218348x99000293.

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The box-count method is used to obtain fractal dimensions, limiting probabilities and relative entropies for fractal and fractal-like objects generated by matrix and summation substitution systems. Results are based on the fact that a transition matrix formalism can be developed for these systems, yielding box counts at each iteration. The transition matrix is non-negative, and many results on non-negative matrices can be applied. The formalism developed is, up to a multiplicative constant, equivalent to a Markov process.

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22

Shen, Hang, Zheng Nie, Jie Xia, and Qibin Wang. "A Markov method for a mechanical system reliability assessment using discrete degradation data." Journal of Physics: Conference Series 2815, no. 1 (2024): 012048. http://dx.doi.org/10.1088/1742-6596/2815/1/012048.

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Abstract A homogenous continuous-time Markov model (HCTMM) has the advantage of high accuracy and is usually used in reliability analysis. However, the transition intensities (rates) between any two states cannot be accurately obtained due to the discrete data obtained from a mechanical system and little expert knowledge. This paper proposes a method in which the transition intensity matrix of an HCTMM can be obtained indirectly using the actual discrete degradation data of a mechanical system. Firstly, the one-step transition probability matrix can be calculated by a hidden Markov model using

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Qi, Xiao-hui, Dian-qing Li, and Kok-kwang Phoon. "Estimation of horizontal transition probability matrix for coupled Markov chain." Japanese Geotechnical Society Special Publication 2, no. 71 (2016): 2423–28. http://dx.doi.org/10.3208/jgssp.tc304-03.

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24

ZHAO, Yanli, and Xing WANG. "Steganalysis of JPEG images based on bilateral transition probability matrix." Journal of Computer Applications 33, no. 4 (2013): 1074–76. http://dx.doi.org/10.3724/sp.j.1087.2013.01074.

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25

Ching, W. K., X. Chen, and N. K. Tsing. "Generating probabilistic Boolean networks from a prescribed transition probability matrix." IET Systems Biology 3, no. 6 (2009): 453–64. http://dx.doi.org/10.1049/iet-syb.2008.0173.

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26

Padaliya, Meera, Alpesh Leua, Umang B. Patel, Savan Padaliya, and Nidhishree R. "Diversification and transition probability matrix of major crops in Gujarat." International Journal of Agriculture Extension and Social Development 8, no. 6 (2025): 417–23. https://doi.org/10.33545/26180723.2025.v8.i6f.2057.

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27

Lu, Jicang, Fenlin Liu, and Xiangyang Luo. "A study on JPEG steganalytic features: Co-occurrence matrix vs. Markov transition probability matrix." Digital Investigation 12 (March 2015): 1–14. http://dx.doi.org/10.1016/j.diin.2014.12.001.

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28

Wang, Xia, Gongshuo Han, Jianing Tang, and Zhongbin Dai. "Complete-Coverage Path-Planning Algorithm Based on Transition Probability and Learning Perturbation Operator." Sensors 25, no. 11 (2025): 3283. https://doi.org/10.3390/s25113283.

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To achieve shorter path length and lower repetition rate for robotic complete coverage path planning, a complete-coverage path-planning algorithm based on transition probability and learning perturbation operator (CCPP-TPLP) is proposed. Firstly, according to the adjacency information between nodes, the distance matrix and transition probability matrix of the accessible grid are established, and the optimal initialization path is generated by applying greedy strategy on the transition probability matrix. Secondly, the population is divided into four subgroups, and different degrees of learning

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29

Hartfiel, D. J. "Homogeneous Markov chains by bounded transition matrix." Journal of Applied Probability 31, no. 2 (1994): 362–72. http://dx.doi.org/10.2307/3215029.

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Let A be a stochastic matrix and ε a positive number. We consider all stochastic matrices within ε of A and their corresponding stochastic eigenvectors. A convex polytope containing these vectors is described. An efficient algorithm for computing bounds on the components of these vectors is also given. The work is compared to previous such work done by the author and by Courtois and Semai.

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30

Bonsdorff, Heikki. "On the Convergence Rate of Bonus-Malus Systems." ASTIN Bulletin 22, no. 2 (1992): 217–23. http://dx.doi.org/10.2143/ast.22.2.2005116.

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AbstractUnder certain conditions, a Bonus-Malus system can be interpreted as a Markov chain whose n-step transition probabilities converge to a limit probability distribution. In this paper, the rate of the convergence is studied by means of the eigenvalues of the transition probability matrix of the Markov chain.

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Zhai, Y., J. Liu, J. Du, and J. Chen. "SOLUTION TO FLEET SIZE OF DOCKLESS BIKE-SHARING STATION BASED ON MATRIX ANALYSIS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4 (September 19, 2018): 255–62. http://dx.doi.org/10.5194/isprs-annals-iv-4-255-2018.

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<p><strong>Abstract.</strong> Aiming at the problems of the lack of reasonable judgment of fleet size and non-optimization of rebalancing for dockless bike-sharing station, based on the usage characteristics of dockless bike-sharing, this paper demonstrates that the Markov chain is suitable for the analysis of the fleet size of station. It is concluded that dockless bike-sharing Markov chain probability limit state (steady-state) only exists and is independent of the initial probability distribution. On that basis, this paper analyses the difficulty of the transition probabil

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32

Remillard, Gilbert, and James M. Clark. "Generating fixed-length sequences satisfying any givennth-order transition probability matrix." Behavior Research Methods, Instruments, & Computers 31, no. 2 (1999): 235–43. http://dx.doi.org/10.3758/bf03207715.

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33

Gupta, S., S. Chattopadhyay, and P. Gunda. "Generalized Implementation of Em Algorithm for Estimation of Transition Probability Matrix." Value in Health 18, no. 7 (2015): A694. http://dx.doi.org/10.1016/j.jval.2015.09.2585.

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34

Phatarfod, R. M., A. J. Pryde, and David Dyte. "On the move-to-front scheme with Markov dependent requests." Journal of Applied Probability 34, no. 3 (1997): 790–94. http://dx.doi.org/10.2307/3215104.

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In this paper we consider the operation of the move-to-front scheme where the requests form a Markov chain of N states with transition probability matrix P. It is shown that the configurations of items at successive requests form a Markov chain, and its transition probability matrix has eigenvalues that are the eigenvalues of all the principal submatrices of P except those of order N—1. We also show that the multiplicity of the eigenvalues of submatrices of order m is the number of derangements of N — m objects. The last result is shown to be true even if P is not a stochastic matrix.

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Voorhees, Burton, and Catherine Beauchemin. "Point Mutations and Transitions Between Cellular Automata Attractor Basins." Complex Systems 15, no. 1 (2004): 41–78. http://dx.doi.org/10.25088/complexsystems.15.1.41.

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Transformations between attractor basins of binary cylindrical cellular automata resulting from mutations are considered. A tau-point mutation of a state consists in toggling tau sites in that state. Results of such mutations are described by a rule-dependent probability matrix. The structure of this matrix is studied in relation to the structure of the state transition diagram and several theorems relating these are proved for the case of additive rules. It is shown that the steady state of the Markov process defined by the probability matrix is always the uniform distribution over the state

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Phatarfod, R. M., A. J. Pryde, and David Dyte. "On the move-to-front scheme with Markov dependent requests." Journal of Applied Probability 34, no. 03 (1997): 790–94. http://dx.doi.org/10.1017/s0021900200101445.

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In this paper we consider the operation of the move-to-front scheme where the requests form a Markov chain of N states with transition probability matrix P . It is shown that the configurations of items at successive requests form a Markov chain, and its transition probability matrix has eigenvalues that are the eigenvalues of all the principal submatrices of P except those of order N—1. We also show that the multiplicity of the eigenvalues of submatrices of order m is the number of derangements of N — m objects. The last result is shown to be true even if P is not a stochastic matrix.

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Li, Quan-Lin, and Yiqiang Q. Zhao. "Heavy-tailed asymptotics of stationary probability vectors of Markov chains of gi/g/1 type." Advances in Applied Probability 37, no. 2 (2005): 482–509. http://dx.doi.org/10.1239/aap/1118858635.

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In this paper, we provide a novel approach to studying the heavy-tailed asymptotics of the stationary probability vector of a Markov chain of GI/G/1 type, whose transition matrix is constructed from two matrix sequences referred to as a boundary matrix sequence and a repeating matrix sequence, respectively. We first provide a necessary and sufficient condition under which the stationary probability vector is heavy tailed. Then we derive the long-tailed asymptotics of the R-measure in terms of the RG-factorization of the repeating matrix sequence, and a Wiener-Hopf equation for the boundary mat

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Li, Quan-Lin, and Yiqiang Q. Zhao. "Heavy-tailed asymptotics of stationary probability vectors of Markov chains of gi/g/1 type." Advances in Applied Probability 37, no. 02 (2005): 482–509. http://dx.doi.org/10.1017/s0001867800000276.

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In this paper, we provide a novel approach to studying the heavy-tailed asymptotics of the stationary probability vector of a Markov chain of GI/G/1 type, whose transition matrix is constructed from two matrix sequences referred to as a boundary matrix sequence and a repeating matrix sequence, respectively. We first provide a necessary and sufficient condition under which the stationary probability vector is heavy tailed. Then we derive the long-tailed asymptotics of the R-measure in terms of the RG-factorization of the repeating matrix sequence, and a Wiener-Hopf equation for the boundary mat

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Шаршеев, Э. С., Б. М. Жакеев, У. Т. Чортомбаев, and И. Д. Базарбаева. "ANALYSIS OF CLIMATIC CONDITIONS WITH THE APPARATUS OF THE MATRIX OF TRANSITION PROBABILITIES." НАУКА, НОВЫЕ ТЕХНОЛОГИИ И ИННОВАЦИИ КЫРГЫЗСТАНА, no. 3 (March 31, 2022): 25–33. http://dx.doi.org/10.26104/nntik.2022.47.88.005.

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В научной работе рассмотрена случайные процессы изменения входящих величин при математическом моделировании климатических условий внешней среды. В качестве случайных процессов рассматривались выпадения атмосферных осадков, изменение во времени температуры воздуха и относительной влажности воздуха. Посредством инструмента матриц переходных вероятностей сделан анализ вышеназванных климатических условий. В строчках матрицы переходных вероятностей можно увидеть внутреннюю структуру и свойства случайных процессов, в связи с этим при анализе внешней среды или при рассмотрении изменения климата реком

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Kenduiywoa, B. K., D. Bargiel, and U. Soergel. "SPATIAL-TEMPORAL CONDITIONAL RANDOM FIELDS CROP CLASSIFICATION FROM TERRASAR-X IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W4 (March 11, 2015): 79–86. http://dx.doi.org/10.5194/isprsannals-ii-3-w4-79-2015.

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The rapid increase in population in the world has propelled pressure on arable land. Consequently, the food basket has continuously declined while global demand for food has grown twofold. There is need to monitor and update agriculture land-cover to support food security measures. This study develops a spatial-temporal approach using conditional random fields (CRF) to classify co-registered images acquired in two epochs. We adopt random forest (RF) as CRF association potential and introduce a temporal potential for mutual crop phenology information exchange between spatially corresponding sit

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41

Liu, Jian, Wenfeng Liu, Yuhu Cheng, Shuguang Ge, and Xuesong Wang. "Similarity Network Fusion Based on Random Walk and Relative Entropy for Cancer Subtype Prediction of Multigenomic Data." Scientific Programming 2021 (August 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/2292703.

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It is a crucial task to design an integrated method to discover cancer subtypes and understand the heterogeneity of cancer based on multiple genomic data. In recent years, some clustering algorithms have been proposed and applied to cancer subtype prediction. Among them, similarity network fusion (SNF) can integrate multiple types of genomic data to identify cancer subtypes, which improves the understanding of tumorigenesis. SNF uses a dense similarity matrix to obtain the global information of the data, and the interconnection of samples between different categories will cause noise interfere

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Andradóttir, Sigrún, Daniel P. Heyman, and Teunis J. Ott. "Potentially unlimited variance reduction in importance sampling of Markov chains." Advances in Applied Probability 28, no. 1 (1996): 166–88. http://dx.doi.org/10.2307/1427916.

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We consider the application of importance sampling in steady-state simulations of finite Markov chains. We show that, for a large class of performance measures, there is a choice of the alternative transition matrix for which the ratio of the variance of the importance sampling estimator to the variance of the naive simulation estimator converges to zero as the sample path length goes to infinity. Obtaining this ‘optimal’ transition matrix involves computing the performance measure of interest, so the optimal matrix cannot be computed in precisely those situations where simulation is required

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Andradóttir, Sigrún, Daniel P. Heyman, and Teunis J. Ott. "Potentially unlimited variance reduction in importance sampling of Markov chains." Advances in Applied Probability 28, no. 01 (1996): 166–88. http://dx.doi.org/10.1017/s0001867800027312.

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We consider the application of importance sampling in steady-state simulations of finite Markov chains. We show that, for a large class of performance measures, there is a choice of the alternative transition matrix for which the ratio of the variance of the importance sampling estimator to the variance of the naive simulation estimator converges to zero as the sample path length goes to infinity. Obtaining this ‘optimal’ transition matrix involves computing the performance measure of interest, so the optimal matrix cannot be computed in precisely those situations where simulation is required

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44

Harbowo, Danni Gathot, and Triyana Muliawati. "State transition matrix and Markov-chain diagram for frequent volcanic eruptions: Krakatoa, Indonesia." E3S Web of Conferences 479 (2024): 02005. http://dx.doi.org/10.1051/e3sconf/202447902005.

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Krakatoa has been a global attraction throughout history. Historical records of eruptions on this volcanic island complex provide thrill and excitement for visitors at once. They are often stunned by sudden eruptions, noticed by columns of ash billowing high into the sky and drawing attention to the scene. However, eruptions such as this also risk visitors of an improper radius. Therefore, in this study, we aimed to reveal the probability pattern of eruptions to sustain preparedness for the worst. Through the Anakrakatau eruption dataset that we collected from 2018 to 2023 [n=540], we propose

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Orpych, Robert. "Zastosowanie łańcuchów Markowa w badaniu stopnia stabilności dochodów podatników." Wiadomości Statystyczne. The Polish Statistician 2013, no. 5 (2013): 22–35. http://dx.doi.org/10.59139/ws.2013.05.2.

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The paper presents the results of an empirical analysis of the stability of taxpayers’ income paying in one of the Silesian tax offices. To assess the degree of income stability the author used Markov chains. Transition probability matrix was constructed, which was used to estimate the synthetic indicator of income stability. The analysis found that the income of taxpayers were characterized by a low degree of stability. The research stated that the probability of transition to a higher income group was lower than the probability of transition to a lower income group, and with the increase in

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Wokoma, D. S. A., and I. U. Amadi. "The Influence of Markov Chain and Properties of Principal Component Solutions in The Analysis of Share Price Movements for Stock Market." British Journal of Multidisciplinary and Advanced Studies 4, no. 6 (2023): 23–35. http://dx.doi.org/10.37745/bjmas.2022.0377.

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The stock market performance and operation has been widely recognized as a significantly viable investment field in financial markets. Therefore, this paper studied stochastic analysis of Markov chain and PCA in the closing share price data of Access and Fidelity banks (2016-2022) through Nigeria Stock Exchange. The share prices were transformed into 3-steps transition probability matrix solution to cover this number of years. The striking focus of this is on two merged banks where their transition probability matrix was considered. The future share prices changes were known. The criteria of o

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U Amadi,, I., C. P. Ogbogbo, and B. O Osu,. "Stochastic analysis of stock price changes as markov chain in finite states." Global Journal of Pure and Applied Sciences 28, no. 1 (2022): 91–98. http://dx.doi.org/10.4314/gjpas.v28i1.11.

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In this work, stochastic analysis of Markov chain model used to examine stock price formation in finite states. The data was subjected to 5-step transition matrix for independent stocks where transition matrix replicated the use of 3-states transition probability matrix. This enables us proffer precise condition of obtaining expected mean rate of return of each stock. Out of the four stocks studied, stock (1), stock (2), stock (3) and stock (4), it was also discovered that stock (1) has the highest mean rate of return:4.0548 and Stock (4) has the best probability of price increasing in the nea

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Endo, Kenta, Ippei Mimura, and Yusuke Sawada. "Hypergroups and distance distributions of random walks on graphs." MATHEMATICA SCANDINAVICA 127, no. 1 (2021): 43–62. http://dx.doi.org/10.7146/math.scand.a-122932.

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Wildberger's construction enables us to obtain a hypergroup from a random walk on a special graph. We will give a probability theoretic interpretation to products on the hypergroup. The hypergroup can be identified with a commutative algebra whose basis is transition matrices. We will estimate the operator norm of such a transition matrix and clarify a relationship between their matrix products and random walks.

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Mostafizur Rahman, Md, and M. Sayedur Rahman. "MONITORING DROUGHT VULNERABILITY AT DIFFERENT TIME SCALES: A CASE STUDY FROM RAJSHAHI DISTRICT, BANGLADESH." International Journal of Advanced Research 8, no. 11 (2020): 404–14. http://dx.doi.org/10.21474/ijar01/12022.

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Drought is a slow onset natural disaster which creates a threat to social and agro-ecological balance. The failure of rain and the occurrence of drought during any particular growing season may lead to severe food shortage and increase vulnerability. The purpose of this study is to estimate the Drought Index (DI) in multiple time scales for Rajshahi district of Bangladesh.The 52 years daily rainfall data during the period 1964-2015 used for analysis. There were some missing data over that range. We separated this time interval in 5, 7 and 10 days series format then we estimate missing value by

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Qiu, Li, Xiaopin Zhong, Shanbin Li, and Bugong Xu. "Output Feedback Stabilization of Networked Control Systems with Uncertain Transition Probability Matrix." IFAC Proceedings Volumes 46, no. 20 (2013): 292–97. http://dx.doi.org/10.3182/20130902-3-cn-3020.00126.

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