Готові списки джерел за темами / Bayesian regulation

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Добірка наукової літератури з теми "Bayesian regulation"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Bayesian regulation"

Zhao, Ying Ding, and Xian Xia Qiao. "Review of Modeling Methods of Gene Expression Regulation Networks." Applied Mechanics and Materials 433-435 (October 2013): 783–87. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.783.

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the study of Gene expression regulation network is expected to fully disclose function and behavior of the genome from the perspective of system. Now, Gene expression regulation network construction methods include Boolean networks, linear combination, the weighted matrix, Bayesian networks and differential equation, And so on, These methods are real abstraction of gene expression regulation network at different levels. Boolean network is qualitative research network of gene expression regulating, And Differential equation is quantified to describe biological processes by detailed mathematical

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Holmgren, Noél Michael André, Niclas Norrström, Robert Aps, and Sakari Kuikka. "A Concept of Bayesian Regulation in Fisheries Management." PLoS ONE 9, no. 11 (2014): e111614. http://dx.doi.org/10.1371/journal.pone.0111614.

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Muhammad, Afiq Wiratma, and Bhina Patria. "Self-Regulation Effects on Smoking Addiction: A Bayesian Approach." Universitas Psychologica 20 (December 15, 2021): 1–10. http://dx.doi.org/10.11144/javeriana.upsy20.sres.

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This study aimed to determine the impacts of self-regulation on smoking addiction. Self-regulation is the capacity to overcome ineffective behavior to achieve goals. Previous research studies found that self-regulation is an effective mental defense against deviant behaviors such as smoking. This study hypothesized that self-regulation correlates negatively with smoking addiction. The subjects in this study were 155 students aged 13-18 years (M = 13.37, SD = 1.07) from three high schools in Sleman district, Yogyakarta, Indonesia. The data were analysed with Bayesian regression and simple linea

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Sitanggang, Sahat Sonang, Sarjon Defit, and Mukhlis Ramadhan. "Analisis Optimasi Fungsi Pelatihan Machine Learning Neural Network dalam Peramalan Kemiskinan." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 7, no. 3 (2021): 359. http://dx.doi.org/10.26418/jp.v7i3.50092.

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Banyak metode fungsi pelatihan dalam Machine Learning Neural Network yang digunakan dalam menyelesaikan masalah komputasi yang berkaitan dengan prediksi. Fungsi pelatihan yang digunakan pada Machine Learning metoda algoritma backpropagation dapat menghasilkan prediksi yang berbeda, yang dipengaruhi oleh parameter dan data yang digunakan. Tujuan dari penelitian dilakukan untuk menganalisa performance dan keakuratan algoritma backpropagation standard serta mengoptimalkan fungsi pelatihan dengan algoritma Bayesian Regulation, dan One Step Secant. Dalam proses analisis, penelitian ini menggunakan

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Guo, Yingni, and Eran Shmaya. "Robust Monopoly Regulation." American Economic Review 115, no. 2 (2025): 599–634. https://doi.org/10.1257/aer.20191950.

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We study how to regulate a monopolistic firm using a robust-design, non-Bayesian approach. We derive a policy that minimizes the regulator’s worst-case regret, where regret is the difference between the regulator’s complete-information payoff and his realized payoff. When the regulator’s payoff is consumers’ surplus, he caps the firm’s average revenue. When his payoff is the total surplus of both consumers and the firm, he offers a piece rate subsidy to the firm while capping the total subsidy. For intermediate cases, the regulator combines these three policy instruments to balance three goals

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Sabatti, Chiara, and Gareth M. James. "Bayesian sparse hidden components analysis for transcription regulation networks." Bioinformatics 22, no. 6 (2005): 739–46. http://dx.doi.org/10.1093/bioinformatics/btk017.

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Li, Mingliang, Kevin J. Mumford, and Justin L. Tobias. "A Bayesian analysis of payday loans and their regulation." Journal of Econometrics 171, no. 2 (2012): 205–16. http://dx.doi.org/10.1016/j.jeconom.2012.06.010.

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Wanto, Anjar, Sarjon Defit, and Agus Perdana Windarto. "Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 2 (2021): 254–64. http://dx.doi.org/10.29207/resti.v5i2.3031.

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Research has been carried out with several training functions using standard backpropagation methods, One-Step Secant (OSS), and Bayesian regulation. The purpose of this study was to (i) analyze the Performance accuracy (Performance) of the standard backpropagation method and (ii) optimize the training function with the One-Step Secant (OSS) and Bayesian regulation methods to obtain comparison results of the three methods in the search for the best results implementation of disaster phenomenon forecasting data. The research method is based on quantitative methods with times-series data on disa

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Rodgers, W., and HO Tucker. "Neurological Mechanisms in Immune Regulation." International Journal Of Biology And Medicine 1, no. 1 (2019): 88–92. https://doi.org/10.36811/ijbm.2019.110011.

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This paper is a survey of the neurological mechanisms involved in the regulation of immune function. Its specific focus is to explore the anatomical regions of the brain that mediate inflammatory responses throughout the body. It begins by briefly reviewing experiments that elucidate a connection between psychological process and immune function. It also introduces the physiological connections that enable communication between the central nervous system and peripheral immune system. It then examines the brain regions involved in regulating immune responses, with additional insights drawn from

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Song, Lei, and Xiao Qing Hu. "Fitting the GPS/Leveling Quasi-Geoid Using Bayesian-Regulation BP Neural Network." Applied Mechanics and Materials 90-93 (September 2011): 2903–6. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.2903.

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The 2.5′×2.5′resolution local quasi-geoid is calculated using the global gravity field model and GPS/leveling data of region which points spacing is about 10km with the Bayesian- regulation BP neural network in this paper. The inner and outer precision of quasi-geoid are both superior 0.05m.The result indicat that the Bayesian regulation BP neural network could improve the precision of fitting and restrain the over-fitting in fitting. The region quasi-geoid excelled than 0.05m can be computed using the global gravity field model and about 10km baseline GPS/leveling data in smoothness region.

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Більше джерел

Дисертації з теми "Bayesian regulation"

Ocone, Andrea. "Variational inference for Gaussian-jump processes with application in gene regulation." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8280.

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In the last decades, the explosion of data from quantitative techniques has revolutionised our understanding of biological processes. In this scenario, advanced statistical methods and algorithms are becoming fundamental to decipher the dynamics of biochemical mechanisms such those involved in the regulation of gene expression. Here we develop mechanistic models and approximate inference techniques to reverse engineer the dynamics of gene regulation, from mRNA and/or protein time series data. We start from an existent variational framework for statistical inference in transcriptional networks.

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Grau, Jan [Verfasser], Stefan [Akademischer Betreuer] Posch, and Alexander [Akademischer Betreuer] Schliep. "Discriminative Bayesian principles for predicting sequence signals of gene regulation / Jan Grau. Betreuer: Stefan Posch ; Alexander Schliep." Halle, Saale : Universitäts- und Landesbibliothek Sachsen-Anhalt, 2010. http://d-nb.info/102501068X/34.

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Whyte, Alonzo. "Attention regulation and behavioural flexibility in rats, with relevance to schizophrenia." Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/10483.

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Schizophrenia is a neuropsychological disorder in which the neural systems which regulate attention allocation, primarily the dorsolateral prefrontal cortex, are dysfunctional, resulting in deficient gating of attention to irrelevant inputs from the environment. This sensory processing dysfunction hinders goal-directed behaviour to the extent that the subsequent cognitive deficits of schizophrenia prevent many chronic patients from leading normal lives. It is the onus of neuroscience to understand the nature of deficits induced by the disorder, thus providing target mechanisms for remediation

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Domenech, Philippe. "Une approche neuro-computationnelle de la prise de décision et de sa régulation contextuelle." Phd thesis, Université Claude Bernard - Lyon I, 2011. http://tel.archives-ouvertes.fr/tel-00847494.

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Décider, c'est sélectionner une alternative parmi l'ensemble des options possibles en accord avec nos buts. Les décisions perceptuelles, correspondant à la sélection d'une action sur la base d'une perception, résultent de l'accumulation progressive d'information sensorielle jusqu'à un seuil de décision. Aux niveaux comportemental et cérébral, ce processus est bien capturé par les modèles de décision par échantillonnage séquentiel. L'étude neurobiologique des processus de décision, guidée par l'usage de modèles computationnels, a permis d'établir un lien clair entre cette accumulation d'informa

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Salazar, Cortés Jean Carlo. "Contribution to reliable control of dynamic systems." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/669250.

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This thesis presents sorne contributions to the field of Health-Aware Control (HAC) of dynamic systems. In the first part of this thesis, a review of the concepts and methodologies related to reliability versus degradation and fault tolerant control versus health-aware control is presented. Firstly, in an attempt to unify concepts, an overview of HAC, degradation, and reliability modeling including some of the most relevant theoretical and applied contributions is given. Moreover, reliability modeling is formalized and exemplified using the structure function, Bayesian networks (BNs) and Dyn

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Robinson, Joshua Westly. "Modeling Time-Varying Networks with Applications to Neural Flow and Genetic Regulation." Diss., 2010. http://hdl.handle.net/10161/3109.

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<p>Many biological processes are effectively modeled as networks, but a frequent assumption is that these networks do not change during data collection. However, that assumption does not hold for many phenomena, such as neural growth during learning or changes in genetic regulation during cell differentiation. Approaches are needed that explicitly model networks as they change in time and that characterize the nature of those changes.</p><p>In this work, we develop a new class of graphical models in which the conditional dependence structure of the underlying data-generation process is permi

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Joseph, Shannon Adina. "The effectiveness of Bayesian Belief Networks and real-time monitoring technology in facilitating the implementation of water quality regulations." 2007. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=788849&T=F.

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Книги з теми "Bayesian regulation"

1945-, Gilbert Richard J., and Jacquemin Alexis, eds. Barriers to entry and strategic competition. Routledge, 2001.

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Частини книг з теми "Bayesian regulation"

Ebrahimijam, Saeed, Cahit Adaoglu, and Korhan K. Gokmenoglu. "Inter-Market Sentiment Analysis Using Markov Switching Bayesian VAR Analysis." In Regulation of Finance and Accounting. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99873-8_6.

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Gong, Dongdong, Jie Feng, Wenjing Xiao, and Shan Sun. "Spectral Reconstruction Based on Bayesian Regulation Neural Network." In Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3863-6_9.

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Vogelsang, Ingo. "A Non-Bayesian Incentive Mechanism Using Two-Part Tariffs." In Price Caps and Incentive Regulation in Telecommunications. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3976-6_2.

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Modrák, Martin. "Reparametrizing the Sigmoid Model of Gene Regulation for Bayesian Inference." In Computational Methods in Systems Biology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99429-1_20.

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Bolduc, Denis, and Sylvie Bonin. "Bayesian Analysis of Road Accidents: A General Framework for The Multinomial Case." In Automobile Insurance: Road Safety, New Drivers, Risks, Insurance Fraud and Regulation. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4058-8_4.

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Stanaway, M. "Evidence of absence for invasive species: roles for hierarchical Bayesian approaches in regulation." In Biosecurity surveillance: quantitative approaches. CABI, 2015. http://dx.doi.org/10.1079/9781780643595.0265.

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Thach, Nguyen Ngoc, Nguyen Duc Trung, and Nguyen Thi Nhu Quynh. "Impact of Monetary and Macroprudential Regulation on Bank Stability: A Bayesian Mixed-Effects Analysis." In Prediction and Causality in Econometrics and Related Topics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77094-5_27.

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Azadeh, A., M. Partovi, M. Saberi, Elizabeth Chang, and Omar Hussain. "A Bayesian Network for Improving Organizational Regulations Effectiveness: Concurrent Modeling of Organizational Resilience Engineering and Macro-Ergonomics Indicators." In Advances in Intelligent Networking and Collaborative Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65636-6_25.

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"Bayesian statistics." In Statistical Thinking for Non-Statisticians in Drug Regulation. John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118470961.ch15.

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Irony, Telba, and Lei Huang. "Bayesian Approaches in the Regulation of Medical Products." In Bayesian Applications in Pharmaceutical Development. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315099798-14.

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Тези доповідей конференцій з теми "Bayesian regulation"

Wang, Xiaocheng, Pengjiao Sun, Na Wang, and Jia Guo. "Bayesian Game-Based Comfort Trading Strategies for Temperature Regulation in Smart Grids." In 2024 IEEE 3rd Industrial Electronics Society Annual On-Line Conference (ONCON). IEEE, 2024. https://doi.org/10.1109/oncon62778.2024.10931635.

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Cao, Xinyu, Yifan Song, Jiayuan Wang, Linyu Zhu, and Xi Chen. "Bayesian Optimization for Enhancing Spherical Crystallization Derived from Emulsions: A Case Study on Ibuprofen." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.158833.

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The pharmaceutical industry is a highly specialized field where strict quality control and accelerated time-to-market are essential for maintaining competitive advantage. Spherical crystallization has emerged as a promising approach in pharmaceutical manufacturing, offering significant potential to reduce equipment and operating costs, enhancing drug bioavailability, and facilitating compliance with product quality regulations. Emulsions, as an enabling technology for spherical crystallization, present unique advantages. However, the quality of spherical crystallization products derived from e

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Zhang, Yan. "On the Genetic Regulation of Bayesian Networks." In 2019 12th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2019. http://dx.doi.org/10.1109/iscid.2019.10139.

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"A Bayesian approach to lipophilicity prediction." In Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) :. Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022. http://dx.doi.org/10.18699/sbb-2022-132199.

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Ajanki, Antti, Janne Nikkila, and Samuel Kaski. "Discovering Condition-Dependent Bayesian Networks for Gene Regulation." In 2007 IEEE International Workshop on Genomic Signal Processing and Statistics. IEEE, 2007. http://dx.doi.org/10.1109/gensips.2007.4365846.

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Lv, Zezhou, Jinya Su, and Shihua Li. "Bayesian Parameter Auto-Tuning for ADRC Based PMSM Speed Regulation." In 2024 IEEE 18th International Conference on Advanced Motion Control (AMC). IEEE, 2024. http://dx.doi.org/10.1109/amc58169.2024.10505648.

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Elvitigala, Thanura R., Abhay K. Singh, Himadri B. Pakrasi, and Bijoy K. Ghosh. "Bayesian network approach to understand regulation of biological processes in cyanobacteria." In 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5399570.

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Fangjihu, First A. "An Application Based on Levenberg-Marquardt Bayesian Regulation Algorithm in Power Plate." In 2018 China International Conference on Electricity Distribution (CICED). IEEE, 2018. http://dx.doi.org/10.1109/ciced.2018.8592341.

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Zhou, Wei, and Yingfei Sun. "Gene Regulation Network Modeling via Improved Multi-agent System and Dynamic Bayesian Network." In 2015 2nd International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2015. http://dx.doi.org/10.1109/icisce.2015.174.

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Звіти організацій з теми "Bayesian regulation"

Lawrence, Charles E., Lee Newberg, LeeAnn McCue, and Williams Thomspon. Bayesian computational approaches for gene regulation studies of bioethanol and biohydrogen production. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1183981.

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Newberg, Lee, Lee Anne McCue, and Patrick Van Roey. Bayesian Computational Approaches for Gene Regulation Studies of Bioethanol and Biohydrogen Production. Final Scientific/Technical Report. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1129075.

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López, Lucía, Florens Odendahl, Susana Párraga, and Edgar Silgado-Gómez. The pass-through to inflation of gas price shocks. Banco de España, 2025. https://doi.org/10.53479/39118.

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This paper uses a Bayesian Structural Vector Autoregressive (BSVAR) framework to estimate the pass-through of unexpected gas price supply shocks to HICP inflation in the euro area and its four largest economies. Compared with oil price shocks, gas price shocks have an approximately one-third smaller pass-through to headline inflation. Country-specific results indicate that gas price increases matter more for German, Spanish and Italian inflation than for French inflation, hinging on the reliance on energy commodities in consumption, production and different electricity price regulations. Consi

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Kingston, A. W., A. Mort, C. Deblonde, and O H Ardakani. Hydrogen sulfide (H2S) distribution in the Triassic Montney Formation of the Western Canadian Sedimentary Basin. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329266.

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The Montney Formation is a highly productive hydrocarbon reservoir that is of great economic importance to Canada, however production is often dogged by the presence of hydrogen sulfide (H2S), a highly toxic and corrosive gas. Mapping H2S distribution across the Montney basin in the Western Canadian Sedimentary Basin (WCSB) is fundamental to understanding the processes responsible for its occurrence. We derive a Montney-specific dataset of well gas and water geochemistry from the publically available archives of the Alberta Energy Regulator (AER) and British Columbia Oil and Gas Commission (BC

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Mis-, Dis-, and Malinformation: A Game-Theoretic Analysis of Costly Truth and Equilibrium Outcomes in Digital Media Ecosystems. CIRANO, 2025. https://doi.org/10.54932/nfef9727.

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This article develops a Bayesian game-theoretic model to analyze the persistence and widespread prevalence of misinformation, disinformation, and malinformation within contemporary media ecosystems. Using the classic “Two Generals Problem” metaphor, we conceptualize information transmission as a strategic coordination game under conditions of uncertainty, emphasizing critical factors such as costs associated with truthful content production (??), reliability of message dissemination (?), payoffs for truthful versus misleading communication (?? , ?? ), audience composition (?), and algorithmic

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