Relevant bibliographies by topics / Bayesian statistical decision theory – Applications

Academic literature on the topic 'Bayesian statistical decision theory – Applications'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Bayesian statistical decision theory – Applications"

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Procaccia, H., R. Cordier, and S. Muller. "Application of Bayesian statistical decision theory for a maintenance optimization problem." Reliability Engineering & System Safety 55, no. 2 (February 1997): 143–49. http://dx.doi.org/10.1016/s0951-8320(96)00006-3.

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Laedermann, Jean-Pascal, Jean-François Valley, and François O. Bochud. "Measurement of radioactive samples: application of the Bayesian statistical decision theory." Metrologia 42, no. 5 (September 13, 2005): 442–48. http://dx.doi.org/10.1088/0026-1394/42/5/015.

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Luce, Bryan R., Ya-Chen Tina Shih, and Karl Claxton. "INTRODUCTION." International Journal of Technology Assessment in Health Care 17, no. 1 (January 2001): 1–5. http://dx.doi.org/10.1017/s0266462301104010.

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Until the mid-1980s, most economic analyses of healthcare technologies were based on decision theory and used decision-analytic models. The goal was to synthesize all relevant clinical and economic evidence for the purpose of assisting decision makers to efficiently allocate society's scarce resources. This was true of virtually all the early cost-effectiveness evaluations sponsored and/or published by the U.S. Congressional Office of Technology Assessment (OTA) (15), Centers of Disease Control and Prevention (CDC), the National Cancer Institute, other elements of the U.S. Public Health Service, and of healthcare technology assessors in Europe and elsewhere around the world. Methodologists routinely espoused, or at minimum assumed, that these economic analyses were based on decision theory (8;24;25). Since decision theory is rooted in—in fact, an informal application of—Bayesian statistical theory, these analysts were conducting studies to assist healthcare decision making by appealing to a Bayesian rather than a classical, or frequentist, inference approach. But their efforts were not so labeled. Oddly, the statistical training of these decision analysts was invariably classical, not Bayesian. Many were not—and still are not—conversant with Bayesian statistical approaches.
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Abraham, Christophe. "Asymptotics in Bayesian decision theory with applications to global robustness." Journal of Multivariate Analysis 95, no. 1 (July 2005): 50–65. http://dx.doi.org/10.1016/j.jmva.2004.07.001.

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Garrett, K. A., L. V. Madden, G. Hughes, and W. F. Pfender. "New Applications of Statistical Tools in Plant Pathology." Phytopathology® 94, no. 9 (September 2004): 999–1003. http://dx.doi.org/10.1094/phyto.2004.94.9.999.

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The series of papers introduced by this one address a range of statistical applications in plant pathology, including survival analysis, nonparametric analysis of disease associations, multivariate analyses, neural networks, meta-analysis, and Bayesian statistics. Here we present an overview of additional applications of statistics in plant pathology. An analysis of variance based on the assumption of normally distributed responses with equal variances has been a standard approach in biology for decades. Advances in statistical theory and computation now make it convenient to appropriately deal with discrete responses using generalized linear models, with adjustments for overdispersion as needed. New nonparametric approaches are available for analysis of ordinal data such as disease ratings. Many experiments require the use of models with fixed and random effects for data analysis. New or expanded computing packages, such as SAS PROC MIXED, coupled with extensive advances in statistical theory, allow for appropriate analyses of normally distributed data using linear mixed models, and discrete data with generalized linear mixed models. Decision theory offers a framework in plant pathology for contexts such as the decision about whether to apply or withhold a treatment. Model selection can be performed using Akaike's information criterion. Plant pathologists studying pathogens at the population level have traditionally been the main consumers of statistical approaches in plant pathology, but new technologies such as microarrays supply estimates of gene expression for thousands of genes simultaneously and present challenges for statistical analysis. Applications to the study of the landscape of the field and of the genome share the risk of pseudoreplication, the problem of determining the appropriate scale of the experimental unit and of obtaining sufficient replication at that scale.
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Borysova, Valentyna I., and Bohdan P. Karnaukh. "Standard of proof in common law: Mathematical explication and probative value of statistical data." Journal of the National Academy of Legal Sciences of Ukraine 28, no. 2 (June 25, 2021): 171–80. http://dx.doi.org/10.37635/jnalsu.28(2).2021.171-180.

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As a result of recent amendments to the procedural legislation of Ukraine, one may observe a tendency in judicial practice to differentiate the standards of proof depending on the type of litigation. Thus, in commercial litigation the so-called standard of “probability of evidence” applies, while in criminal proceedings – “beyond a reasonable doubt” standard applies. The purpose of this study was to find the rational justification for the differentiation of the standards of proof applied in civil (commercial) and criminal cases and to explain how the same fact is considered proven for the purposes of civil lawsuit and not proven for the purposes of criminal charge. The study is based on the methodology of Bayesian decision theory. The paper demonstrated how the principles of Bayesian decision theory can be applied to judicial fact-finding. According to Bayesian theory, the standard of proof applied depends on the ratio of the false positive error disutility to false negative error disutility. Since both types of error have the same disutility in a civil litigation, the threshold value of conviction is 50+ percent. In a criminal case, on the other hand, the disutility of false positive error considerably exceeds the disutility of the false negative one, and therefore the threshold value of conviction shall be much higher, amounting to 90 percent. Bayesian decision theory is premised on probabilistic assessments. And since the concept of probability has many meanings, the results of the application of Bayesian theory to judicial fact-finding can be interpreted in a variety of ways. When dealing with statistical evidence, it is crucial to distinguish between subjective and objective probability. Statistics indicate objective probability, while the standard of proof refers to subjective probability. Yet, in some cases, especially when statistical data is the only available evidence, the subjective probability may be roughly equivalent to the objective probability. In such cases, statistics cannot be ignored
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Mukha, V. S., and N. F. Kako. "The integrals and integral transformations connected with the joint vector Gaussian distribution." Proceedings of the National Academy of Sciences of Belarus. Physics and Mathematics Series 57, no. 2 (July 16, 2021): 206–16. http://dx.doi.org/10.29235/1561-2430-2021-57-2-206-216.

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In many applications it is desirable to consider not one random vector but a number of random vectors with the joint distribution. This paper is devoted to the integral and integral transformations connected with the joint vector Gaussian probability density function. Such integral and transformations arise in the statistical decision theory, particularly, in the dual control theory based on the statistical decision theory. One of the results represented in the paper is the integral of the joint Gaussian probability density function. The other results are the total probability formula and Bayes formula formulated in terms of the joint vector Gaussian probability density function. As an example the Bayesian estimations of the coefficients of the multiple regression function are obtained. The proposed integrals can be used as table integrals in various fields of research.
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Girtler, Jerzy. "Limiting Distribution of the Three-State Semi-Markov Model of Technical State Transitions of Ship Power Plant Machines and its Applicability in Operational Decision-Making." Polish Maritime Research 27, no. 2 (June 1, 2020): 136–44. http://dx.doi.org/10.2478/pomr-2020-0035.

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AbstractThe article presents the three-state semi-Markov model of the process {W(t): t ≥ 0} of state transitions of a ship power plant machine, with the following interpretation of these states: s1 – state of full serviceability, s2 – state of partial serviceability, and s3 – state of unserviceability. These states are precisely defined for the ship main engine (ME). A hypothesis is proposed which explains the possibility of application of this model to examine models of real state transitions of ship power plant machines. Empirical data concerning ME were used for calculating limiting probabilities for the process {W(t): t ≥ 0}. The applicability of these probabilities in decision making with the assistance of the Bayesian statistical theory is demonstrated. The probabilities were calculated using a procedure included in the computational software MATHEMATICA, taking into consideration the fact that the random variables representing state transition times of the process {W(t): t ≥ 0} have gamma distributions. The usefulness of the Bayesian statistical theory in operational decision-making concerning ship power plants is shown using a decision dendrite which maps ME states and consequences of particular decisions, thus making it possible to choose between the following two decisions: d1 – first perform a relevant preventive service of the engine to restore its state and then perform the commissioned task within the time limit determined by the customer, and d2 – omit the preventive service and start performing the commissioned task.
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Liu, Shun, Qin Xu, and Pengfei Zhang. "Identifying Doppler Velocity Contamination Caused by Migrating Birds. Part II: Bayes Identification and Probability Tests." Journal of Atmospheric and Oceanic Technology 22, no. 8 (August 1, 2005): 1114–21. http://dx.doi.org/10.1175/jtech1758.1.

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Abstract Based on the Bayesian statistical decision theory, a probabilistic quality control (QC) technique is developed to identify and flag migrating-bird-contaminated sweeps of level II velocity scans at the lowest elevation angle using the QC parameters presented in Part I. The QC technique can use either each single QC parameter or all three in combination. The single-parameter QC technique is shown to be useful for evaluating the effectiveness of each QC parameter based on the smallness of the tested percentages of wrong decision by using the ground truth information (if available) or based on the smallness of the estimated probabilities of wrong decision (if there is no ground truth information). The multiparameter QC technique is demonstrated to be much better than any of the three single-parameter QC techniques, as indicated by the very small value of the tested percentages of wrong decision for no-flag decisions (not contaminated by migrating birds). Since the averages of the estimated probabilities of wrong decision are quite close to the tested percentages of wrong decision, they can provide useful information about the probability of wrong decision when the multiparameter QC technique is used for real applications (with no ground truth information).
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Ma, Rui, Long Han, and Hujun Geng. "Implementation and Error Analysis of MNIST Handwritten Dataset Classification Based on Bayesian Decision Classifier." Journal of Physics: Conference Series 2171, no. 1 (January 1, 2022): 012049. http://dx.doi.org/10.1088/1742-6596/2171/1/012049.

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Abstract In recent years, with the continuous development of computer technology, pattern recognition technology has gradually entered people’s life and learning, and people’s demand for pattern recognition technology is also growing.In order to adapt to people’s life and study, the application of pattern recognition theory is more and more, such as speech recognition, character recognition, face recognition and so on.The main methods of pattern recognition are statistics, clustering,neural network and artificial intelligence.Statistical method is one of the most classic methods, and Bayesian classification is widely used in statistical method because of its convenience and good classification effect.
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Dissertations / Theses on the topic "Bayesian statistical decision theory – Applications"

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Chiu, Jing-Er. "Applications of bayesian methods to arthritis research /." free to MU campus, to others for purchase, 2001. http://wwwlib.umi.com/cr/mo/fullcit?p3036813.

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Luo, Wuben. "A comparative assessment of Dempster-Shafer and Bayesian belief in civil engineering applications." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/28500.

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The Bayesian theory has long been the predominate method in dealing with uncertainties in civil engineering practice including water resources engineering. However, it imposes unnecessary restrictive requirements on inferential problems. Concerns thus arise about the effectiveness of using Bayesian theory in dealing with more general inferential problems. The recently developed Dempster-Shafer theory appears to be able to surmount the limitations of Bayesian theory. The new theory was originally proposed as a pure mathematical theory. A reasonable amount of work has been done in trying to adopt this new theory in practice, most of this work being related to inexact inference in expert systems and all of the work still remaining in the fundamental stage. The purpose of this research is first to compare the two theories and second to try to apply Dempster-Shafer theory in solving real problems in water resources engineering. In comparing Bayesian and Dempster-Shafer theory, the equivalent situation between these two theories under a special situation is discussed first. The divergence of results from Dempster-Shafer and Bayesian approaches under more general situations where Bayesian theory is unsatisfactory is then examined. Following this, the conceptual difference between the two theories is argued. Also discussed in the first part of this research is the issue of dealing with evidence including classifying sources of evidence and expressing them through belief functions. In attempting to adopt Dempster-Shafer theory in engineering practice, the Dempster-Shafer decision theory, i.e. the application of Dempster-Shafer theory within the framework of conventional decision theory, is introduced. The application of this new decision theory is demonstrated through a water resources engineering design example.
Applied Science, Faculty of
Civil Engineering, Department of
Graduate
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So, Moon-tong. "Applications of Bayesian statistical model selection in social science research." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B39312951.

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Nono, Bertin. "Applications of Bayesian statistics a thesis presented to the faculty of the Graduate School, Tennessee Technological University /." Click to access online, 2009. http://proquest.umi.com/pqdweb?index=3&did=1769600741&SrchMode=1&sid=3&Fmt=6&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1250263533&clientId=28564.

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Ignatieva, Ekaterina. "Adaptive Bayesian sampling with application to 'bubbles'." Connect to e-thesis, 2008. http://theses.gla.ac.uk/356/.

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Thesis (MSc(R)) - University of Glasgow, 2008.
MSc(R). thesis submitted to the Department of Mathematics, Faculty of Information and Mathematical Sciences, University of Glasgow, 2008. Includes bibliographical references.
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So, Moon-tong, and 蘇滿堂. "Applications of Bayesian statistical model selection in social scienceresearch." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39312951.

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Lu, Jun. "Bayesian hierarchical models and applications in psychology research /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144437.

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Dümbgen, Moritz. "Extremal martingales with applications and a Bayesian approach to model selection." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708881.

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Higdon, David. "Spatial applications of Markov chain Monte Carlo for Bayesian inference /." Thesis, Connect to this title online; UW restricted, 1994. http://hdl.handle.net/1773/8942.

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Zhang, Yanwei. "A hierarchical Bayesian approach to model spatially correlated binary data with applications to dental research." Diss., Connect to online resource - MSU authorized users, 2008.

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Books on the topic "Bayesian statistical decision theory – Applications"

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Koch, Karl-Rudolf. Bayesian inferencewith geodetic applications. Berlin: Springer-Verlag, 1990.

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P, Tsokos Chris, ed. Bayesian theory and methods with applications. Amsterdam: Atlantis Press, 2011.

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Koch, Karl-Rudolf. Bayesian inference with geodetic applications. Berlin: Springer-Verlag, 1990.

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Current trends in Bayesian methodology with applications. Boca Raton, FL: CRC Press, 2015.

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Subjective and objective Bayesian statistics: Principles, models, and applications. 2nd ed. Hoboken, N.J: Wiley-Interscience, 2003.

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Link, William A. Bayesian inference: With ecological applications. Amsterdam: Academic Press/Elsevier, 2010.

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Bayesian approach to global optimization: Theory and applications. Dordrecht: Kluwer Academic, 1989.

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Pourret, Olivier. Bayesian networks: A practical guide to applications. Chichester, West Sussex, Eng: John Wiley, 2008.

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A, Bell David, ed. Evidence theory and its applications. Amsterdam: North-Holland, 1991.

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E, Holmes Dawn, Jain L. C, and SpringerLink (Online service), eds. Innovations in Bayesian Networks: Theory and Applications. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.

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Book chapters on the topic "Bayesian statistical decision theory – Applications"

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Berger, J. O., B. Boukai, and Y. Wang. "Properties of Unified Bayesian-Frequentist Tests." In Advances in Statistical Decision Theory and Applications, 207–23. Boston, MA: Birkhäuser Boston, 1997. http://dx.doi.org/10.1007/978-1-4612-2308-5_14.

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Liang, TaChen. "On Hierarchical Bayesian Estimation and Selection for Multivariate Hypergeometric Distributions." In Advances in Statistical Decision Theory and Applications, 49–64. Boston, MA: Birkhäuser Boston, 1997. http://dx.doi.org/10.1007/978-1-4612-2308-5_4.

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Bayarri, M. J., and James O. Berger. "Applications and Limitations of Robust Bayesian Bounds and Type II MLE." In Statistical Decision Theory and Related Topics V, 121–34. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2618-5_10.

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Longford, Nicholas T. "The Bayesian Paradigm." In Statistical Decision Theory, 49–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40433-7_4.

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Kachiashvili, K. J. "Constrained Bayesian Rules for Testing Statistical Hypotheses." In Strategic Management, Decision Theory, and Decision Science, 159–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1368-5_11.

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Diaconis, Persi. "Bayesian Numerical Analysis." In Statistical Decision Theory and Related Topics IV, 163–75. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4613-8768-8_20.

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Zellner, Arnold. "Bayesian and Non-Bayesian Estimation Using Balanced Loss Functions." In Statistical Decision Theory and Related Topics V, 377–90. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2618-5_28.

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Bernardo, José M. "Bayesian Linear Probabilistic Classification." In Statistical Decision Theory and Related Topics IV, 151–62. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4613-8768-8_19.

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Chen, Ming-Hui, Dipak K. Dey, Peter Müller, Dongchu Sun, and Keying Ye. "Objective Bayesian Inference with Applications." In Frontiers of Statistical Decision Making and Bayesian Analysis, 31–68. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6944-6_2.

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Bernardo, José M. "Bayesian Estimation of Political Transition Matrices." In Statistical Decision Theory and Related Topics V, 135–40. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2618-5_11.

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Conference papers on the topic "Bayesian statistical decision theory – Applications"

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Boulanger, Pierre, and Guy Godin. "Multiresolution segmentation of range images based on Bayesian decision theory." In Applications in Optical Science and Engineering, edited by David P. Casasent. SPIE, 1992. http://dx.doi.org/10.1117/12.131542.

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"Data Visualisation and Statistical Analysis within the Decision Making Process." In International Conference on Information Visualization Theory and Applications. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004212604890494.

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Takahashi, Hayato. "Bayesian approach to a definition of random sequences and its applications to statistical inference." In 2006 IEEE International Symposium on Information Theory. IEEE, 2006. http://dx.doi.org/10.1109/isit.2006.261937.

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Kim, Taewung, and Hyun-Yong Jeong. "A Crash Prediction Algorithm Using a Particle Filter and Bayesian Decision Theory." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12118.

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Active safety systems have been developed in automotive industry, and a tracking algorithm and a threat assessment algorithm are needed in such systems to predict the collision between vehicles. It is difficult to track a threat vehicle accurately because of lack of information on a threat vehicle and the measurement noise which does normally not follow Gaussian distribution. Therefore, there is an uncertainty whether the collision will occur or not. Particle filtering is widely used for nonlinear and non-Gaussian tracking problems, and statistical decision theory can be used to make an optimal decision in an uncertain case. In this study, a crash prediction algorithm has been developed using a particle filter and statistical decision making.
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Caucci, Luca, Harrison H. Barrett, Nicholas Devaney, and Jeffrey J. Rodríguez. "Statistical Decision Theory and Adaptive Optics: A Rigorous Approach to Exoplanet Detection." In Adaptive Optics: Methods, Analysis and Applications. Washington, D.C.: OSA, 2007. http://dx.doi.org/10.1364/aopt.2007.atua5.

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Husmeier, Dirk, Umberto Noe, Agnieszka Borowska, Hao Gao, Alan Lazarus, Vinny Davies, Benn Macdonald, Colin Berry, and Xiaoyu Luo. "Statistical Emulation of Cardiac Mechanics: An Important Step towards a Clinical Decision Support System." In International Conference on Statistics: Theory and Applications (ICSTA'19). Avestia Publishing, 2019. http://dx.doi.org/10.11159/icsta19.29.

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Vadde, S., J. K. Allen, and F. Mistree. "Catalog Design: Design Using Available Assets." In ASME 1992 Design Technical Conferences. American Society of Mechanical Engineers, 1992. http://dx.doi.org/10.1115/detc1992-0139.

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Abstract Catalog design is a procedure in which a system is assembled by selecting standard components from catalogs of available components. Selection in design involves making a choice among a number of alternatives taking into account several attributes. The information available to a designer to do so during the early stages of project initiation may be uncertain. The uncertainty in information may be imprecise or stochastic. Under these circumstances, a designer has to balance limited resources against the quality of solution obtained or decisions made by accounting for uncertainty in information available. This complex task becomes formidable when dealing with coupled selection problems, that is problems that should be solved simultaneously. Coupled selection problems share a number of coupling attributes among them. In an earlier paper we have shown how selection problems, both coupled and uncoupled can be reformulated as a single compromise Decision Support Problem (DSP) using a deterministic model. In this paper, we show how the traditional compromise DSP can be extended to represent a nondeterministic case. We use fuzzy set theory to model imprecision and Bayesian statistics to model stochastic information. Formulations that can be solved with the same solution scheme are presented to handle both fuzzy and stochastic information in the standard framework of a compromise DSP. The approaches are illustrated by an example involving the coupled selection of a heat exchanger concept and a cooling fluid for a specific application. The emphasis in this paper is placed on explaining the methods.
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Aslam, Usman, Luis Hernando Perez Cardenas, and Andrey Klimushin. "Application of an Integrated Ensemble-Based History Matching Approach - An Offshore Field Case Study." In SPE Trinidad and Tobago Section Energy Resources Conference. SPE, 2021. http://dx.doi.org/10.2118/200908-ms.

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Abstract The Internet of Things has popularized the notion of a digital twin - a virtual representation of a physical system. There are substantial risks associated with designing a development plan for an oilfield and the industry has been making use of reservoir models - digital twins - to improve the decision-making process for many years. With an increase in the availability of computational resources, the industry is moving towards ensemble-based workflows to estimate risk in field development plans. In this paper, we demonstrate the use of an integrated ensemble-based approach to assess uncertainties in the reservoir models and quantify their impact on the decision-making process. An important feature of a digital twin is its ability to use sensor data to update the virtual model, more commonly known as history matching or data assimilation. We demonstrate how production data can be used to identify and constrain the uncertainties in the reservoir model. Production data is incorporated using Bayesian statistics and state-of-the-art supervised machine learning techniques to create an ensemble of models that capture the range of uncertainties in the reservoir model. This ensemble of calibrated models with an improved predictive ability provides a realistic assessment of the uncertainty associated with production forecasts. The ensemble-based approach is demonstrated through its application on an offshore oilfield located in the North Sea. The field is highly compartmentalized and has high structural uncertainty following the interpretation and depth conversion. An integrated cross-domain model is set up to incorporate typically ignored structural uncertainty in addition to the uncertainties and their dependencies in the dynamic parameters, including fault transmissibility, pore-volume, fluid contacts, saturation, and relative permeability endpoints, etc. Results from the history matched ensemble of models show a significa nt reduction in uncertainty in these parameters and the predicted production. An advantage of the proposed technique is that the automated, repeatable, and auditable ensemble-based workflow can assimilate the newly acquired measured data into the reservoir model at any time, keeping the model up-to-date and evergreen.
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Mei, Han, Linlin Mao, Yi Zhang, and Meiying Chen. "BDT-ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise Based on Bayesian Decision Theory for Identifying Clusters with Multi-Densities." In 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2022. http://dx.doi.org/10.1109/itaic54216.2022.9836545.

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Ochie, Karen, Moghanloo Rouzbeh, Jamal Daneshfar, and Jeffrey Burghardt. "A Probability Evaluation of Seismicity Risks Associated with CO2 Injection into Arbuckle Formation." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210345-ms.

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Abstract This paper examines the application of Bayes’ theorem to evaluate risk of induced seismicity associated with CO2 sequestration in the Arbuckle Formation, which extends across the southern Mid-Continent of the US. Geological storage can effectively contribute to reducing emission of CO2, otherwise released into the atmosphere, achieving the climate goals committed in the 2021 United Nations Climate Change Conference (COP26), however, concerns about risks associated with CO2 injection along with economic challenges of infrastructure required to execute the Carbon Capture Utilization and Storage projects stand against full realization of remarkable potentials. The main goal is usually for CO2 to be stored over geologic time; hence, geomechanical risks such as the seismicity in the field or potential CO2 leakage through seals cannot be ignored and is considered as one of the requirements to determine success of the project. This paper elaborates the risk of potential seismic events that can impact the longevity and success of projects. Accurate risk estimation is key for environmental, economic, and safety concerns and is also one of the requirements to get class VI permits from the US Environmental Protection Agency. We utilized the Bayesian approach, a statistical model where a random probability distribution is used to represent uncertainties within the model, including both input/output parameters. Using Oklahoma as a case study we utilized data from established physics-based models of the system and the details from past observed/monitored failures to evaluate future risk potential for the area. In our approach, we establish the current probability for the state of stress for the area under investigation, then monitor how the state of stress evolves. The stress state probability distribution is calculated to evaluate the probability of activating a critically oriented fault over a range of specified pore pressures. The results suggest that we can estimate the probability of inducing seismicity in the formation. Based on our modelling results, at initial injection pressuresthere is a 30% risk of introducing seismicity in the Arbuckle formation. Based on these results, we went further to conduct a sensitivity analysis to determine the features with multiple predictor dependence on the risk level. In most cases analyzed the risk of induced seismicity by injection is still greater than 30% due to the stress state being very poorly constrained. Introducing stress state constraints from the Arbuckle formation in Kansas State, the risk of seismicity reduced to 10%. Considering the results from our work, operators can optimize the site screening and collect additional data to constrain inherent uncertainties in geomechanical risk evaluation and make informed decisions during operations. The result from this work shows that geological storage of CO2 at reduced rates in the Arbuckle formation can be a feasible safe strategy towards achieving climate goals in selected areas and there is value of information in obtaining stress data in these areas.
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