Cited by 415Probability and Statistical ResearchMarkov Chains and Monte Carlo MethodsStochastic processes and statistical mechanics

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Markov Chains: From Theory to Implementation and Experimentation

Gagniuc, Paul A·2017·Wiley eBooks

DOI 10.1002/9781119387596

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Yes We can: Watermarking Machine Learning Models beyond Classification
Lounici, S., Njeh, M., Ermiş, O., Önen, M., & Trabelsi, S. (2021) · IEEE Computer Security Foundations Workshop (CSFW) · doi:10.1109/csf51468.2021.00044 · Cited by 13
“In order to do that, the adversary can build a random string detector based on Markov Chains [20].”
Landmark Privacy: Configurable Differential Privacy Protection for Time Series
Katsomallos, M., Tzompanaki, K., & Kotzinos, D. (2022) · ACM Conference on Data and Application Security and Privacy (CODASPY) · doi:10.1145/3508398.3511501 · Cited by 12
Gaze Information Channel in Cognitive Comprehension of Poster Reading
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PPTPF: Privacy-Preserving Trajectory Publication Framework for CDR Mobile Trajectories
Yang, J., Dash, M., & Teo, S. G. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10040224 · Cited by 11
Artificial Intelligence in Education
Hussain, S. (2024) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-031-51244-5_27 · Cited by 12
Urban morphology and climate vulnerability assessment in Kuwait: A spatio-temporal predictive analysis utilizing deep neural network-enhanced markov chain models for 2050 and 2100
Al-Shaar, W., Lehmann, X., Saad, N. G. E.-D., et al. (2025) · PLoS ONE · doi:10.1371/journal.pone.0318604 · Cited by 11
A Systematic Review of Application Progress on Machine Learning-Based Natural Language Processing in Breast Cancer over the Past 5 Years
Li, C., Weng, Y., Zhang, Y., & Wang, B. (2023) · Diagnostics · doi:10.3390/diagnostics13030537 · Cited by 11
Analyzing the effect size of urban growth driving factors: application of multilayer-perceptron Markov-chain model for the Riyadh city
Al-Shaar, W. (2023) · Modeling Earth Systems and Environment · doi:10.1007/s40808-023-01781-8 · Cited by 11
“The predictive probability theory revealing that the results of future experiments are dependent to some extent on the outcomes of historical chance is known as the Markov Chain (Al-Shaar et al. 2020; Gagniuc 2017).”
Risk-based, sensor-fused detection of flooding casualties for emergency response
Karolius, K. B., Cichowicz, J., & Vassalos, D. (2020) · Ships and Offshore Structures · doi:10.1080/17445302.2020.1735846 · Cited by 11
“The posterior update in time can be interpreted as a recursive state transition of a Markovian process (Gagniuc, 2017) in which the next state 𝑥𝑘 depends solely on the immediately preceding state 𝑥𝑘−1.”
Background
A general model of conversational dynamics and an example application in serious illness communication
Clarfeld, L. A., Gramling, R. E., Rizzo, D. M., & Eppstein, M. J. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253124 · Cited by 11
Incorporation of causality structures to complex network analysis of time-varying behaviour of multivariate time series
Carlos-Sandberg, L., & Clack, C. D. (2021) · Scientific Reports · doi:10.1038/s41598-021-97741-2 · Cited by 11
Application of Hidden Markov Models to Analyze, Group and Visualize Spatio-Temporal COVID-19 Data
Zhou, S., Braca, P., Maranò, S., Willett, P., Millefiori, L. M., Gaglione, D., & Pattipati, K. R. (2021) · IEEE Access · doi:10.1109/access.2021.3114364 · Cited by 11
“This constraint implies that all the HMM states are strongly connected and that every hidden state is reachable from every other hidden state [40].”
Background
Distributed Principal Subspace Analysis for Partitioned Big Data: Algorithms, Analysis, and Implementation
Gang, A., Xiang, B., & Bajwa, W. U. (2021) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2021.3122297 · Cited by 12
“3 show that S-DOT and SA-DOT do not perform too well since ring topology is a periodic Markov chain [33] that cannot converge to a steady-state distribution.”
Background
Exploring factors influencing urban sprawl and land-use changes analysis using systematic points and random forest classification
Jamali, A. A., Behnam, A., Almodaresi, S. A., He, S., & Jaafari, A. (2023) · Environment Development and Sustainability · doi:10.1007/s10668-023-03633-y · Cited by 11
Markov Chain Signal Generation Based on Single Magnetic Tunnel Junction
Yuan, X., Jian, J., Chai, Z., et al. (2023) · IEEE Electron Device Letters · doi:10.1109/led.2023.3322194 · Cited by 11
Large Language Models
Mitchell, M. (2024) · doi:10.21428/e2759450.2bb20e3c · Cited by 13
Arid and semiarid rangeland responses to non-stationary temporal dynamics of environmental drivers
Easdale, M. H., Perri, D. V., & Bruzzone, O. A. (2022) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2022.100796 · Cited by 10
Analyzing Dynamic Operational Conditions of Limb Prosthetic Sockets with a Mechatronics-Twin Framework
Chen, D., Su, P., Ottikkutti, S., Vartholomeos, P., Tahmasebi, K. N., & Karamousadakis, M. (2022) · Applied Sciences · doi:10.3390/app12030986 · Cited by 11
DRMM: A novel data mining-based emotion transfer detecting method for emotion prediction of social media
Shi, W., Xue, G., Yin, X., et al. (2022) · Journal of Information Science · doi:10.1177/01655515221100728 · Cited by 10
“This article contributes in predicting the trend of netizens’ emotional state by combining association rules [1–3] and Markov Chain [4].”
Method
Functional imaging of conduction dynamics in cortical and spinal axons
Radivojević, M., & Punga, A. R. (2023) · eLife · doi:10.7554/elife.86512 · Cited by 10
Variable star classification using multiview metric learning
Johnston, K., Caballero-Nieves, S. M., Pétit, V., et al. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz3165 · Cited by 10
“…identification has varied between generating machine learned features (Bos et al. 2002; Broersen 2009; Blomme et al. 2011; Bolós and Benítez 2014; Gagniuc 2017), implementing generic features (e.g. Fourier domain features; Debosscher 2009; Richards et al. 2012; Palaversa et al. 2013; Masci et…”
Background
Kinetic model for a sol-gel transition: application of the modified Bailey criterion
Suman, K., & Joshi, Y. M. (2020) · Rheologica Acta · doi:10.1007/s00397-020-01237-1 · Cited by 10
“To this objective, we make use of an umbrella formalism associated with theMarkovian hypothesis that has applications in a variety of fields together with, but not limited to, science, music, and social science including economics (Gagniuc 2017; Kampen 1981).”
Method
Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System
Yu, H. Q. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-63092-8_3 · Cited by 10
Between pebbles and organisms: weaving autonomy into the Markov blanket
van Es, T., & Kirchhoff, M. D. (2021) · Synthese · doi:10.1007/s11229-021-03084-w · Cited by 10
“Yet being in a steady state implies that the system remains in the same state over time (Gagniuc, 2017).”
Background
An integrated modelling framework for complex systems safety analysis
Tolo, S., & Andrews, J. (2022) · Quality and Reliability Engineering International · doi:10.1002/qre.3212 · Cited by 10
A Systematic Review of Recent Models for Agri-Food Supply Chain Management With Emphasis on the Application of Artificial Intelligence and Sustainability
Herrera-Granda, I. D., Alemany, M. M. E., Peluffo-Ordóñez, D. H., Herrera-Granda, E. P., & D’Ambrosio, G. (2026) · IEEE Access · doi:10.1109/access.2026.3657974 · Cited by 11
“In addition, Markov Chains (MC) and Monte Carlo Experiments (MCE) are powerful tools for simulating dynamic systems and evaluating scenarios under uncertainty [25], [26].”
Graph reconstruction and attraction method for community detection
Wu, X., Teng, D., Zhang, H., et al. (2025) · Applied Intelligence · doi:10.1007/s10489-024-05858-4 · Cited by 9
“In GRAM, we extract network structure information of a graph by introducing a new passing probability matrix based on Markov Chains [12] by which a new graph is further reconstructed, and modularity optimization is adopted on the reconstructed one instead of the original one for non-overlapping…”
Modeling methods for strategy formulation in a turbulent environment
Gavrilova, T. A., Kubelskiy, M., Kudryavtsev, D., et al. (2018) · Strategic Change · doi:10.1002/jsc.2209 · Cited by 9
Context-aware knowledge selection and reliable model recommendation with ACCORDION
Ahmed, Y. M., Telmer, C. A., Zhou, G., et al. (2024) · Frontiers in Systems Biology · doi:10.3389/fsysb.2024.1308292 · Cited by 9
A Comparison of Markov Chain Random Field and Ordinary Kriging Methods for Calculating Soil Texture in a Mountainous Watershed, Northwest China
Li, J., Zhang, L., He, C., et al. (2018) · Sustainability · doi:10.3390/su10082819 · Cited by 9
“It describes stochastic processes with discrete state space or discrete index set [36].”
Background
Modeling the Switching Behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment
Dimitriadis, S. I., López, M. E., Maestú, F., & Pereda, E. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00542 · Cited by 10
A Stochastic Model for Residential User Activity Simulation
Liu, X., Yang, Y., Li, R., et al. (2019) · Energies · doi:10.3390/en12173326 · Cited by 9
“The theory of Markov chain and some practical application can be found in [21,22].”
Background
A detection metric designed for O’Connell effect eclipsing binaries
Johnston, K. B., Haber, R., Caballero-Nieves, S. M., et al. (2019) · Computational Astrophysics and Cosmology · doi:10.1186/s40668-019-0031-2 · Cited by 9
Business as Unusual: A Model for Continuous Real-Time Business Insights Based on Low Level Metrics
Figalist, I., Elsner, C., Bosch, J., et al. (2019) · Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa.2019.00019 · Cited by 9
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Jongeneel, G., Greuter, M. J. E., van Erning, F. N., Koopman, M., Medema, J. P., Kandimalla, R., Goel, A., Bujanda, L., Meijer, G. A., Fijneman, R. J. A., et al. (2020) · The European Journal of Health Economics · doi:10.1007/s10198-020-01199-4 · Cited by 9
A Dynamic Model of Cournot Competition for an Oligopolistic Market
Lian, Z., & Zheng, J. (2021) · Mathematics · doi:10.3390/math9050489 · Cited by 9
An optimal System of Lie Subalgebras and Group-Invariant Solutions with Conserved Currents of a (3+1)-D Fifth-Order Nonlinear Model I with Applications in Electrical Electronics, Chemical Engineering and Pharmacy
Adeyemo, O. D., & Khalique, C. M. (2023) · Journal of Nonlinear Mathematical Physics · doi:10.1007/s44198-022-00101-5 · Cited by 9
Graph alignment exploiting the spatial organization improves the similarity of brain networks
Calissano, A., Papadopoulo, T., Pennec, X., & Deslauriers‐Gauthier, S. (2024) · Human Brain Mapping · doi:10.1002/hbm.26554 · Cited by 7
“In FAQ, such initialization matrices has to be a doubly stochastic matrix (Gagniuc, 2017).”
Background
Tides and Coastal Geomorphology: The Role of Non-Stationary Processes
Jay, D. A., Devlin, A. T., Idier, D., Prokocki, E. W., & Flick, R. E. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818234-5.00166-8 · Cited by 10
Learning from Multiple Annotator Noisy Labels via Sample-Wise Label Fusion
Gao, Z., Sun, F.-K., Yang, M., Ren, S., Xiong, Z., Engeler, M., Burazer, A., Wildling, L., Daniel, L., & Boning, D. S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20053-3_24 · Cited by 10
Arbitrary Modulation of Average Dwell Time in Discrete-Time Markov Chains Based on Tunneling Magnetoresistance Effect
Yuan, X., Jian, J., Chai, Z., et al. (2024) · IEEE Electron Device Letters · doi:10.1109/led.2024.3394711 · Cited by 8
“M ARKOV process is a stochastic process with a sequence of possible events, in which the probability of the next event depends only on the state attained in the present event, and is independent of the past events [1], [2].”
Background
Markov Chain Analysis of Ship Energy Efficiency
Garbatov, Y., Yalamov, D., & Georgiev, P. (2024) · Energies · doi:10.3390/en17123018 · Cited by 8
Multi-receptive Field: An Adaptive Path Aggregation Graph Neural Framework for Hyperspectral Image Classification
Ding, Y., Zhang, Z., Hu, H., He, F., Cheng, S., & Zhang, Y. (2024) · Springer eBooks · doi:10.1007/978-981-97-8009-9_4 · Cited by 8
A Survey of Markov Model in Reinforcement Learning
Gao, T., Chen, B., & Mi, Q. (2022) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic54071.2022.9722618 · Cited by 9
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Phase variation in pneumococcal populations during carriage in the human nasopharynx
Croix, M. D. S., Mitsi, E., Morozov, A. Y., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-58684-2 · Cited by 8
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Karatalay, O., Psaromiligkos, I., Champagne, B., et al. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3054810 · Cited by 8
“Note that the row sums of W and V are normalized to one and since their product is a square matrix, it becomes a right stochastic matrix [32].”
Background
Interacting Multiple-Mode Estimation Using Centroid Fixed Structure for High Precision
Li, G., Wang, Q., Zhang, S., et al. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3328546 · Cited by 7
“However, those estimators cannot jump from one model to the other [5], [6], [7].”
Background
Ensure: Towards Reliable Control of Cyber-Physical Systems Under Uncertainty
Yang, W., Xu, C., Pan, M., et al. (2022) · IEEE Transactions on Reliability · doi:10.1109/tr.2022.3167116 · Cited by 7
“A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event [41].”
Background
Modelling and analysis of social engineering threats using the attack tree and the Markov model
Aijaz, M., & Nazir, M. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01540-z · Cited by 7
“SETs are extracted from the existing literature [20] and real-life cases and preprocessed the given data to construct the proposed attack tree and Markov chain.”
Generative artificial intelligence (GAI): From large language models (LLMs) to multimodal applications towards fine tuning of models, implications, investigations
Akhtar, Z. B. (2024) · Computing and artificial intelligence. · doi:10.59400/cai.v3i1.1498 · Cited by 7
“Its ability to generate diverse forms of content, from computer code and essays to creative works like poems, highlights its versatility and potential to revolutionize content creation across various industries [23–33].”
Background
Selfish Mining Time-Averaged Analysis in Bitcoin: Is Orphan Reporting an Effective Countermeasure?
Sarenche, R., Zhang, R., Nikova⋆, S., et al. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3518090 · Cited by 7
Embracing Noise in Chemical Reaction Networks
Enciso, G., & Kim, J. (2019) · Bulletin of Mathematical Biology · doi:10.1007/s11538-019-00575-3 · Cited by 6
“…stochastic models was laid out in B German Enciso enciso@uci.edu 1 Mathematics Department, University of California, Irvine, Irvine, CA, USA G. Enciso, J. Kim the early twentieth century byMarkov and others, with what is now known as discretespace, continuous-time Markov processes (Gagniuc 2017).”
An Effective and Computationally Efficient Approach for Anonymizing Large-Scale Physical Activity Data
Parameshwarappa, P., Chen, Z., & Koru, G. (2020) · International Journal of Information Security and Privacy · doi:10.4018/ijisp.2020070105 · Cited by 7
Classification with LSTM Networks in User Behaviour Analytics with Unbalanced Environment
Parshutin, S., Kirshners, A., Kornijenko, Y., Zabiniako, V., Gasparovica-Asite, M., & Rozkalns, A. (2021) · Automatic Control and Computer Sciences · doi:10.3103/s0146411621010077 · Cited by 7
Analyzing the Omni-Channel Shopper Journey Configuration of Generations Y and Z
Chang, W., & Chen, L.-M. (2021) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.293273 · Cited by 8
“(1) is called the transition probability that the state in time t +1 is only affected by the state in time t , not by the previous states from time 0 to t −1 (Gagniuc, 2017).”
Background
Devising a method for improving the efficiency of artillery shooting based on the Markov model
Boltenkov, V., Brunetkin, O., Dobrynin, Y. V., et al. (2021) · Eastern-European Journal of Enterprise Technologies · doi:10.15587/1729-4061.2021.245854 · Cited by 7
An Actinic Keratosis Auxiliary Diagnosis Method Based on an Enhanced MobileNet Model
Li, S., Li, C., Liu, Q., et al. (2023) · Bioengineering · doi:10.3390/bioengineering10060732 · Cited by 7
Predicting future opportunities and threats of land-use changes on beekeeping activities in Turkey
Sarı, F. (2023) · Environment Development and Sustainability · doi:10.1007/s10668-023-03557-7 · Cited by 6
Technical review on derivation methods for behavior dependent functional responses
Garay, J. (2019) · Community Ecology · doi:10.1556/168.2019.20.1.4 · Cited by 8
“Markov chain is "a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event" (Gagniuc 2017).”
Background
The Many Faces of Agent-Based Computational Economics: Ecology of Agents, Bottom-Up Approaches and Paradigm Shift
Mignot, S., & Vignes, A. (2019) · OEconomia · doi:10.4000/oeconomia.8222 · Cited by 6
Monitoring of forest components reveals that exotic tree species are not always invasive in areas under ecological restoration
Londe, V., de Sousa, H. C., & Messias, M. C. T. B. (2020) · Environmental Monitoring and Assessment · doi:10.1007/s10661-020-08583-w · Cited by 7
“A Markov chain is a stochastic model that describes a sequence of possible events in which the probability of each event depends on the state reached in the previous event (Gagniuc 2017).”
Background
Bayesian Latent Class Analysis: Sample Size, Model Size, and Classification Precision
Mîndrilă, D. (2023) · Mathematics · doi:10.3390/math11122753 · Cited by 7
The Emergence and Evolution of Problematic Properties: Onset, Persistence, Aggravation, and Desistance
O'Brien, D. T., Ristea, A., Tucker, R., et al. (2022) · Journal of Quantitative Criminology · doi:10.1007/s10940-022-09542-z · Cited by 6
“Markov chains are a random process that consists of: a state space, or the profiles of crime and disorder that a parcel might experience; a transition matrix describing the probabilities of transitions between states; and an initial distribution of states (Gagniuc 2017).”
Background
Modelling variability in functional brain networks using embeddings
Huang, R., Gohil, C., & Woolrich, M. W. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1188 · Cited by 6
Limited Scope for Group Coordination in Stylistic Variations of Kolam Art
Tran, N.-H., Kucharský, Š., Waring, T. M., et al. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.742577 · Cited by 6
“Kolam Art as a Markov System Since each kolam loop pattern2 can be decomposed into a sequence of gestures and artists strive to form uniform and smooth loops, the system can be described by a state-based Markov process, whereby the conditional probability distribution for the system at the next step depends only on th…”
Background
An Observationally Trained Markov Model for MJO Propagation
Hagos, S., Leung, L.-Y. R., Zhang, C., et al. (2021) · Geophysical Research Letters · doi:10.1029/2021gl095663 · Cited by 6
“An important point to note is that Equation 2 represents a Markovian model, which is the simplest form in the general class of stochastic models (Gagniuc, 2017; Wilks, 1995), that is, the change in 𝐴𝐴 𝑺𝑺 𝑡𝑡 is assumed to depend only on its previous state 𝐴𝐴 𝑺𝑺 𝑡𝑡 −1 .”
Background
Forecasting of the Employment Rate in the EU ICT Field
Zatonatska, T. H., Klapkiv, Y., Dluhopolskyi, O. V., & Fedirko, O. (2022) · Comparative Economic Research Central and Eastern Europe · doi:10.18778/1508-2008.25.19 · Cited by 9
“The Markov process is a “stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event” (Gagniuc 2017, p. 62).”
Analysis of the Competitiveness in the Agri-food sector: The case of Latin America and the Caribbean Region
Aguiar, G. M. B., & Balogh, J. M. (2022) · Competitio · doi:10.21845/comp/2022/1-2/2 · Cited by 6
Pathways for socio-economic system transitions expressed as a Markov chain
Schweizer, V. J., Jamieson-Lane, A., Cai, H., Lehner, S., & Smerlak, M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0288928 · Cited by 6
“For a full introduction to Markov chains, we refer the reader to [28, 29].”
Background
Teaching Bayesian and Markov methods in business analytics curricula: An integrated approach
Johnson, M., Misra, R. B., & Berenson, M. L. (2021) · Decision Sciences Journal of Innovative Education · doi:10.1111/dsji.12249 · Cited by 6
A Quantitative Evaluation of Thin Slice Sampling for Parent–Infant Interactions
Burgess, R., Costantini, I., Bornstein, M. H., Campbell, A., Cordero, M., Culpin, I., Dingsdale, H., John, R. M., Kennedy, M., Tyson, H. R., et al. (2023) · Journal of Nonverbal Behavior · doi:10.1007/s10919-022-00420-7 · Cited by 6
Sustainable Disaster Response Management Related to Large Technical Systems
Kinzhikeyev, S., Rohács, J., Rohács, D., & Boros, A. (2020) · Sustainability · doi:10.3390/su122410290 · Cited by 6
A Novel Measure Inspired by Lyapunov Exponents for the Characterization of Dynamics in State-Transition Networks
Sándor, B., Schneider, B., Lázár, Z. I., & Ercsey-Ravasz, M. (2021) · Entropy · doi:10.3390/e23010103 · Cited by 7
Modeling Suburban Freeway Travel Variability considering Connected and Autonomous Vehicles
Khorshidi, N. A., Zargari, S. A., Mirzahossein, H., Shakoori, S., & Jin, X. (2024) · International Journal of Civil Engineering · doi:10.1007/s40999-024-01045-1 · Cited by 5
“A Markov chain, or process, is a statistical model that describes potential sequences of events, where each event’s occurrence depends only on the prior event [68].”
Modeling the Short- and Long-Term Impacts of Climate Change on Wheat Production in Egypt Using Autoregressive Distributed Lag Approach
Alboghdady, M. A., Abbas, S., Alashry, M. K., et al. (2025) · Land · doi:10.3390/land14101962 · Cited by 5
Come for syntax, stay for speed, understand defects: an empirical study of defects in Julia programs
Rahman, A., Bose, D. B., Shakya, R., & Pandita, R. (2023) · Empirical Software Engineering · doi:10.1007/s10664-023-10328-5 · Cited by 5
“A Markov Chain is a stochastic model that describes a a sequence of possible events where the probability of each event depends on the state recorded in the previous event (Gagniuc, 2017).”
BackgroundMethod
Regulatory Dynamics of Cell Differentiation Revealed by True Time Series From Multinucleate Single Cells
Pretschner, A., Pabel, S., Haas, M., Heiner, M., & Marwan, W. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2020.612256 · Cited by 5
“It models gene expression trajectories as Markov chains (Gagniuc, 2017), which assumes that each subsequent state only depends on the current state of a cell and not on its previous states, i.e., it does not depend on the individual history of a cell.”
Background
Network Analysis Using Markov Chain Applied to Wildlife Habitat Selection
Dhakal, T., Lim, S. J., Park, Y. C., et al. (2022) · Diversity · doi:10.3390/d14050330 · Cited by 5
Multi-spatiotemporal analysis of changes in mangrove forests in Palawan, Philippines: predicting future trends using a support vector machine algorithm and the Markov chain model
Cayetano, C., Sullivan, E., Miller, P., et al. (2023) · UCL Open Environment · doi:10.14324/111.444/ucloe.000057 · Cited by 5
A robotic reporter still lacks creativity, but it can already replace human journalists in several areas
Macková, V., & Mařík, R. (2023) · Acta Fakulty filozofické · doi:10.24132/actaff.2023.15.1-2.3 · Cited by 6
Determining future scenarios of urban areas with cellular automata/Markov Chain Model method; example of Ereğli District Konya-Türkiye (2030–2040)
AYDIN, T. K., & DURDURAN, S. S. (2024) · Earth Science Informatics · doi:10.1007/s12145-024-01283-w · Cited by 4
“It follows a conditional probability distribution with step-by-step probability transitions in the process (Alsharif et al. 2022; Gagniuc 2017).”
How Ratings Shape the Supply on Sharing Economy Platforms: A Longitudinal Cohort Study on Listing Survival on Airbnb
Kolleck, A., & Teubner, T. (2024) · Information Systems Management · doi:10.1080/10580530.2024.2339234 · Cited by 5
“Understanding ratings (in means of stars) as “states,” Markov chains can intuitively represent the underlying transition processes (Gagniuc, 2017).”
Heracles: A Novel State-based Distributed Verification Framework for DNS Configurations
Hu, K., Du, H., & Wang, Y. (2024) · doi:10.1145/3672199.3673890 · Cited by 5
Health Causal Probability Knowledge Graph
Yu, H. Q. (2020) · International Conference on Bioinformatics Research and Applications (ICBRA) · doi:10.1145/3440067.3440077 · Cited by 5
“Multinomial Naive Bayes [23] or Markov chain algorithm [24].”
Method
Toward Human-Centered Design of Automated Vehicles: A Naturalistic Brake Policy
Rahmati, Y., Abianeh, A. S., Tabesh, M. T., et al. (2021) · Frontiers in Future Transportation · doi:10.3389/ffutr.2021.683223 · Cited by 5
“The Markov property asserts that the future state of a process depends only on the current state, overlooking the future and past states (Gagniuc 2017).”
Background
Reachability Analysis in Stochastic Directed Graphs by Reinforcement Learning
Possieri, C., Frasca, M., & Rizzo, A. (2022) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2022.3143080 · Cited by 5
“Note that the matrix P is right stochastic [14], i.”
Background
A hierarchical Constrained Bayesian (ConBay) approach to jointly estimate water storage and Post-Glacial Rebound from GRACE(-FO) and GNSS data
Forootan, E., & Mehrnegar, N. (2022) · All Earth · doi:10.1080/27669645.2022.2097768 · Cited by 5
“This assumption enables reasoning and computation with the model that would otherwise be intractable (Gagniuc, 2017).”
Background
Model predictive control energy management strategy of fuel cell hybrid electric vehicle
Touil, W., Li, Z., Outbib, R., et al. (2022) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon49645.2022.9968429 · Cited by 5
“4) Markov chain prediction model: With the Markov chain method, a stochastic sequence of future events, wherein the probability of each event depends only on the current state or the process history can be predicted [28].”
Background
Quantum algorithms for powering stable Hermitian matrices
González-García, G., Trivedi, R., & Cirac, J. I. (2021) · Physical Review A · doi:10.1103/physreva.103.062420 · Cited by 5
Delving Deeper into Market Concentration of Poultry Feed and the Driving Factors for Brand Switching: Evidence from Commercial Egg Producers in Nigeria
Sowunmi, F. A., Bello, A. A., Ogunniyi, A. I., & Omotayo, A. O. (2022) · Sustainability · doi:10.3390/su14138030 · Cited by 5
Overview of Recent Advancements in Deep Learning and Artificial Intelligence
Narayanan, V., Cao, Y. K., Panda, P., Challapalle, N. R., Du, X., Kim, Y., Krishnan, G., V, Lee, C., Li, Y., Sun, J., et al. (2023) · Wiley eBooks · doi:10.1002/9781119853923.ch2 · Cited by 7
Real-Time Optimal Route Planning by Deep Reinforcement Learning and Validation with Flight Test
Shim, J., Park, J., Song, N. C., Jang, J., Choi, J.-Y., Lee, G., Prasad, R., & Choi, S. (2023) · AIAA AVIATION Forum · doi:10.2514/6.2023-3623 · Cited by 4
Addressing the Embeddability Problem in Transition Rate Estimation
Goolsby, C., Losey, J., Fakharzadeh, A., Xu, Y., Düker, M., Sherman, M. G., Matteson, D. S., & Moradi, M. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c01367 · Cited by 6
“Building MSMs involves (1) discretization of conformational space, (2) extracting transition statistics from simulation trajectories, and (3) analyzing the transition statistics to estimate kinetic and thermodynamic properties 14 .”
Method
Quantifying uncertainty of mineral prediction using a novel Bayesian deep learning framework
Liu, Y. (2025) · Artificial Intelligence in Geosciences · doi:10.1016/j.aiig.2025.100164 · Cited by 5
The Study of Multivariable Autoregression Methods to Forecast Infectious Diseases
Azhar, M. A. R., Nugroho, H. A., & Wibirama, S. (2021) · IEEE International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) · doi:10.1109/icitisee53823.2021.9655881 · Cited by 4
Logical and probabilistic aspects of state estimation for Markovian systems
Lefebvre, D., Seatzu, C., Hadjicostis, C. N., et al. (2023) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc49753.2023.10383800 · Cited by 4
Perfect simulation of steady-state Markov chain on mixed state space
Zakrad, A.-E., & Nasroallah, A. (2021) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2021.1924783 · Cited by 4
“For more documentation on steady-state Markov chains and their applications, we suggest the following references: Rubinstein and Kroese (2011), Van der Hoek and Elliott (2012), Sericola (2013), Spieler and Wolf (2013), Gallager (2013), Sharma and Vishwakarma (2014) and Gagniuc (2017).”
BackgroundInfluential

Authors: Gagniuc, P. A.
Corresponding author: Gagniuc, Paul A
ORCID: Gagniuc, Paul A
Venue: Wiley eBooks
Publisher: Wiley
Published: 2017-06-22
DOI: 10.1002/9781119387596
OpenAlex: W4235646468
Cited by: 415
References: 0
Citations by year: 2026: 19 · 2025: 37 · 2024: 64 · 2023: 60 · 2022: 54 · 2021: 68 · 2020: 58 · 2019: 38 · 2018: 17
FWCI: 7.99
Type: book
Language: en
Open access: closed
Licence: doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
Locations: Wiley eBooks (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
Physical Sciences › Mathematics › Mathematical Physics › Stochastic processes and statistical mechanics
Keywords: Markov chains, stochastic processes, transition probabilities, Markov property

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