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Relevant bibliographies by topics / Statistics. Bayesian statistical decision theory

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Academic literature on the topic 'Statistics. Bayesian statistical decision theory'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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Journal articles on the topic "Statistics. Bayesian statistical decision theory"

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Wijayanti, Rina. "PENAKSIRAN PARAMETER ANALISIS REGRESI COX DAN ANALISIS SURVIVAL BAYESIAN." PRISMATIKA: Jurnal Pendidikan dan Riset Matematika 1, no. 2 (June 1, 2019): 16–26. http://dx.doi.org/10.33503/prismatika.v1i2.427.

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In the theory of estimation, there are two approaches, namely the classical statistical approach and global statistical approach (Bayesian). Classical statistics are statistics in which the procedure is the decision based only on the data samples taken from the population. While Bayesian statistics in making decisions based on new information from the observed data (sample) and prior knowledge. At this writing Cox Regression Analysis will be taken as an example of parameter estimation by the classical statistical approach Survival Analysis and Bayesian statistical approach as an example of global (Bayesian). Survival Bayesial parameter estimation using MCMC algorithms for model complex / complicated and difficult to resolve while the Cox regression models using the method of partial likelihood. Results of the parameter estimates do not close form that needs to be done by the method of Newton-Raphson iteration.

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Ghosh, Malay, and James Berger. "Stastical Decision Theory and Bayesian Analysis." Journal of the American Statistical Association 83, no. 401 (March 1988): 266. http://dx.doi.org/10.2307/2288950.

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Martín, Jacinto, David Ríos Insua, and Fabrizio Ruggeri. "Joint sensitivity in bayesian decision theory." Test 12, no. 1 (June 2003): 173–94. http://dx.doi.org/10.1007/bf02595818.

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Abraham, Christophe, and Benoît Cadre. "Asymptotic global robustness in bayesian decision theory." Annals of Statistics 32, no. 4 (August 2004): 1341–66. http://dx.doi.org/10.1214/009053604000000562.

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De Waal, D. J. "Summary on Bayes estimation and hypothesis testing." Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie 7, no. 1 (March 17, 1988): 28–32. http://dx.doi.org/10.4102/satnt.v7i1.896.

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Although Bayes’ theorem was published in 1764, it is only recently that Bayesian procedures were used in practice in statistical analyses. Many developments have taken place and are still taking place in the areas of decision theory and group decision making. Two aspects, namely that of estimation and tests of hypotheses, will be looked into. This is the area of statistical inference mainly concerned with Mathematical Statistics.

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de la Horra, Julián. "Bayesian robustness of the quantile loss in statistical decision theory." Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales. Serie A. Matematicas 107, no. 2 (May 16, 2012): 451–58. http://dx.doi.org/10.1007/s13398-012-0070-x.

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Corander, Jukka. "Bayesian graphical model determination using decision theory." Journal of Multivariate Analysis 85, no. 2 (May 2003): 253–66. http://dx.doi.org/10.1016/s0047-259x(02)00033-7.

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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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Geisler, Wilson S., and Randy L. Diehl. "Bayesian natural selection and the evolution of perceptual systems." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 357, no. 1420 (April 29, 2002): 419–48. http://dx.doi.org/10.1098/rstb.2001.1055.

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In recent years, there has been much interest in characterizing statistical properties of natural stimuli in order to better understand the design of perceptual systems. A fruitful approach has been to compare the processing of natural stimuli in real perceptual systems with that of ideal observers derived within the framework of Bayesian statistical decision theory. While this form of optimization theory has provided a deeper understanding of the information contained in natural stimuli as well as of the computational principles employed in perceptual systems, it does not directly consider the process of natural selection, which is ultimately responsible for design. Here we propose a formal framework for analysing how the statistics of natural stimuli and the process of natural selection interact to determine the design of perceptual systems. The framework consists of two complementary components. The first is a maximum fitness ideal observer, a standard Bayesian ideal observer with a utility function appropriate for natural selection. The second component is a formal version of natural selection based upon Bayesian statistical decision theory. Maximum fitness ideal observers and Bayesian natural selection are demonstrated in several examples. We suggest that the Bayesian approach is appropriate not only for the study of perceptual systems but also for the study of many other systems in biology.

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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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Dissertations / Theses on the topic "Statistics. Bayesian statistical decision theory"

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Cheng, Dunlei Stamey James D. "Topics in Bayesian sample size determination and Bayesian model selection." Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5039.

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Ma, Yimin. "Bayesian and empirical Bayesian analysis for the truncation parameter distribution families /." *McMaster only, 1998.

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Fung, Wing-kam Tony. "Analysis of outliers using graphical and quasi-Bayesian methods /." [Hong Kong] : University of Hong Kong, 1987. http://sunzi.lib.hku.hk/hkuto/record.jsp?B1236146X.

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Atherton, Juli. "Bayesian optimal design for changepoint problems." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102954.

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We consider optimal design for changepoint problems with particular attention paid to situations where the only possible change is in the mean. Optimal design for changepoint problems has only been addressed in an unpublished doctoral thesis, and in only one journal article, which was in a frequentist setting. The simplest situation we consider is that of a stochastic process that may undergo a, change at an unknown instant in some interval. The experimenter can take n measurements and is faced with one or more of the following optimal design problems: Where should these n observations be taken in order to best test for a change somewhere in the interval? Where should the observations be taken in order to best test for a change in a specified subinterval? Assuming that a change will take place, where should the observations be taken so that that one may best estimate the before-change mean as well as the after-change mean? We take a Bayesian approach, with a risk based on squared error loss, as a design criterion function for estimation, and a risk based on generalized 0-1 loss, for testing. We also use the Spezzaferri design criterion function for model discrimination, as an alternative criterion function for testing. By insisting that all observations are at least a minimum distance apart in order to ensure rough independence, we find the optimal design for all three problems. We ascertain the optimal designs by writing the design criterion functions as functions of the design measure, rather than of the designs themselves. We then use the geometric form of the design measure space and the concavity of the criterion function to find the optimal design measure. There is a straightforward correspondence between the set of design measures and the set of designs. Our approach is similar in spirit, although rather different in detail, from that introduced by Kiefer. In addition, we consider design for estimation of the changepoint itself, and optimal designs for the multipath changepoint problem. We demonstrate why the former problem most likely has a prior-dependent solution while the latter problems, in their most general settings, are complicated by the lack of concavity of the design criterion function.
Nous considérons, dans cette dissertation, les plans d'expérience bayésiens optimauxpour les problèmes de point de rupture avec changement d'espérance. Un cas de pointde rupture avec changement d'espérance à une seule trajectoire se présente lorsqu'uneséquence de données est prélevée le long d'un axe temporelle (ou son équivalent) etque leur espérance change de valeur. Ce changement, s'il survient, se produit à unendroit sur l'axe inconnu de l'expérimentateur. Cet endroit est appelé "point derupture". Le fait que la position du point de rupture soit inconnue rend les tests etl'inférence difficiles dans les situations de point de rupture à une seule trajectoire.

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Ho, Man Wai. "Bayesian inference for models with monotone densities and hazard rates /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?ISMT%202002%20HO.

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Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 110-114). Also available in electronic version. Access restricted to campus users.

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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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Lau, Wai Kwong. "Bayesian nonparametric methods for some econometric problems /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?ISMT%202005%20LAU.

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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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Woodard, Roger. "Bayesian hierarchical models for hunting success rates /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9951135.

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Oleson, Jacob J. "Bayesian spatial models for small area estimation /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052203.

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Books on the topic "Statistics. Bayesian statistical decision theory"

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1934-, Madansky Albert, and McCulloch Robert E, eds. Elementary Bayesian statistics. Cheltenham, UK: Edward Elgar, 1997.

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Bayesian statistical modelling. 2nd ed. Chichester, England: John Wiley & Sons, 2006.

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Michel, Mouchart, and Rolin J. M, eds. Elements of Bayesian statistics. New York: M. Dekker, 1990.

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Bayesian statistics: An introduction. 2nd ed. London: Arnold, 1997.

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Introduction to Bayesian statistics. 2nd ed. Hoboken, N.J: John Wiley, 2007.

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Lee, Peter M. Bayesian statistics: An introduction. 3rd ed. London: Arnold, 2004.

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Bolstad, William M. Introduction to Bayesian statistics. 2nd ed. Hoboken, NJ: Wiley-Interscience, 2008.

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Bayesian statistics: An introduction. London: E. Arnold, 1992.

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Bayesian statistics: An introduction. New York: Oxford University Press, 1989.

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Bolstad, William M. Computational Bayesian statistics. Hoboken, N.J: Wiley, 2010.

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Book chapters on the topic "Statistics. Bayesian statistical decision theory"

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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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Bernardo, José M. "Simulated Annealing in Bayesian Decision Theory." In Computational Statistics, 547–52. Heidelberg: Physica-Verlag HD, 1992. http://dx.doi.org/10.1007/978-3-662-26811-7_75.

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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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Lewis, Charles, and Dorothy T. Thayer. "Bayesian Decision Theory for Multiple Comparisons." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 326–32. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2009. http://dx.doi.org/10.1214/09-lnms5719.

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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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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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Whitehead, John. "Using Bayesian Decision Theory in Dose-Escalation Studies." In Statistical Methods for Dose-Finding Experiments, 149–71. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470861258.ch7.

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Conference papers on the topic "Statistics. Bayesian statistical decision theory"

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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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Vadde, S., R. S. Krishnamachari, F. Mistree, and J. K. Allen. "The Bayesian Compromise Decision Support Problem for Hierarchical Design Involving Uncertainty." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0088.

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Abstract In this paper we present an extension to the traditional compromise Decision Support Problem (DSP) formulation. In this formulation we use Bayesian Statistics to model uncertainties associated with the information being used. In an earlier paper we have introduced a compromise DSP that accounts for uncertainty using fuzzy set theory. In this paper we describe the Bayesian Decision Support Problem. We use this formulation to design a portal frame structure. We discuss the results and compare them with those obtained using the Fuzzy DSP. Finally, we discuss the efficacy of incorporating Bayesian Statistics into the traditional compromise DSP formulation and describe some of the pending research issues.

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Vadde, S., S. Swadi, N. Bhattacharya, F. Mistree, and J. K. Allen. "Design of an Aircraft Tire: A Study in Modeling Uncertainty." In ASME 1992 Design Technical Conferences. American Society of Mechanical Engineers, 1992. http://dx.doi.org/10.1115/detc1992-0181.

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Abstract During the early stages of project initiation, the information available to a designer may be uncertain (imprecise or stochastic). In response to this need, two extensions of the crisp compromise Decision Support Problem using fuzzy set theory and Bayesian statistics are developed to model uncertainty in design problems. The fuzzy compromise DSP is used to model imprecise information and the Bayesian compromise DSP is used to model stochastic information. The design of an aircraft tire is used as an illustrative example.

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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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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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Hanea, Daniela M., and Ben J. M. Ale. "Estimating the Statistical Distribution of Human Damage Produced by a Fire in a Building Using Bayesian Belief Nets." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79875.

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The complexity of the cities’ layout and other public spaces, together with the large number of people involved leads to increased strain on the resources of emergency responders. An accident, such as a fire, remains a rare event so it is difficult for those in charge of preparing for an emergency and deciding on the acceptability of risk to get a picture of such an event. The interest of all emergency response agencies is to minimize the impact of disaster events on the entities of interest, which include first of all the human population. For this, there is need for a tool that helps the decision makers estimate the distribution of the fire outcome, given different information about the environment in which the fire takes place. This paper discusses the possibility of using continuous Bayesian belief nets for the study of the factors that influence the risk to which the people involved in a building fire are exposed, and how these factors influence the risk. The big advantage of Bayesian belief net approach is that it can model uncertain events. The distribution of the variables of interest can be easily updated given information about some of the other variables. Moreover, the intuitive visual representation of the problem at hand can help people to understand complex systems or processes, like a fire in a building. In this study, the approach is tested for a small example and the results are analyzed. The possibility of extending this method to a more complex model is discussed.

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Hu¨ffmeier, Johannes, Bjo¨rn Forsman, Jim Sandkvist, and Johan Rafstedt. "Decision Support for Offshore Operations in Remote Arctic Areas TOSC: An Optimization Toolbox Based on Bayesian Networks." In ASME 2009 28th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/omae2009-79791.

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SSPA Sweden AB has developed a decision support tool for Arctic offshore operations in close cooperation with the ship owner Transatlantic. With their icebreaking offshore supply vessels, Transatlantic has experience in both offshore operations and ice breaking for the Swedish Maritime Administration, which gives them a unique competence for Arctic offshore tasks. Founded on these experiences SSPA has created a toolbox based on Bayesian networks to provide the decision maker with the required competence to plan, dimension and organise offshore operations. The demands on the model given by Transatlantic for this tool were to include an accurate planning possibility, it should be handy and flexible, successively extendable, based scientifically and it should reflect the operators experience and even experience transfer. The developed tool is based on so called Bayesian Networks. With the help of the graphical directed arrows it is possible to describe complex links and relations between: - specific customer demands and service needs, - supply tasks, icebreaking management, anchor handling, towing, etc., - local external environmental conditions, ice, weather, - surrounding infrastructure, base harbours, transports, - external requirements, national rules, permissions, classification requirements, - possible abnormalities, undesired events, danger of accident, - emergency preparedness, redundant resources, - resources, vessels in use, land-based resources, helicopters, etc. By combining risk analysis methodology, statistics and expert judgements the tool belays and incorporates high safety, cost-benefit, well-reasoned strategies, alternative plans of action and purposive solutions.

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Ciabarri, Fabio, Marco Pirrone, and Cristiano Tarchiani. "ANALYTICAL UNCERTAINTY PROPAGATION IN FACIES CLASSIFICATION WITH UNCERTAIN LOG-DATA." In 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0071.

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Log-facies classification aims to predict a vertical profile of facies at well location with log readings or rock properties calculated in the formation evaluation and/or rock-physics modeling analysis as input. Various classification approaches are described in the literature and new ones continue to appear based on emerging Machine Learning techniques. However, most of the available classification methods assume that the inputs are accurate and their inherent uncertainty, related to measurement errors and interpretation steps, is usually neglected. Accounting for facies uncertainty is not a mere exercise in style, rather it is fundamental for the purpose of understanding the reliability of the classification results, and it also represents a critical information for 3D reservoir modeling and/or seismic characterization processes. This is particularly true in wells characterized by high vertical heterogeneity of rock properties or thinly bedded stratigraphy. Among classification methods, probabilistic classifiers, which relies on the principle of Bayes decision theory, offer an intuitive way to model and propagate measurements/rock properties uncertainty into the classification process. In this work, the Bayesian classifier is enhanced such that the most likely classification of facies is expressed by maximizing the integral product between three probability functions. The latters describe: (1) the a-priori information on facies proportion (2) the likelihood of a set of measurements/rock properties to belong to a certain facies-class and (3) the uncertainty of the inputs to the classifier (log data or rock properties derived from them). Reliability of the classification outcome is therefore improved by accounting for both the global uncertainty, related to facies classes overlap in the classification model, and the depth-dependent uncertainty related to log data. As derived in this work, the most interesting feature of the proposed formulation, although generally valid for any type of probability functions, is that it can be analytically solved by representing the input distributions as a Gaussian mixture model and their related uncertainty as an additive white Gaussian noise. This gives a robust, straightforward and fast approach that can be effortlessly integrated in existing classification workflows. The proposed classifier is tested in various well-log characterization studies on clastic depositional environments where Monte-Carlo realizations of rock properties curves, output of a statistical formation evaluation analysis, are used to infer rock properties distributions. Uncertainty on rock properties, modeled as an additive white Gaussian noise, are then statistically estimated (independently at each depth along the well profile) from the ensemble of Monte-Carlo realizations. At the same time, a classifier, based on a Gaussian mixture model, is parametrically inferred from the pointwise mean of the Monte Carlo realizations given an a-priori reference profile of facies. Classification results, given by the a-posteriori facies proportion and the maximum a-posteriori prediction profiles, are finally computed. The classification outcomes clearly highlight that neglecting uncertainty leads to an erroneous final interpretation, especially at the transition zone between different facies. As mentioned, this become particularly remarkable in complex environments and highly heterogeneous scenarios.

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Allard, Alexandre, Nicolas Fischer, Ian Smith, Peter Harris, and Leslie Pendrill. "Risk calculations for conformity assessment in practice." In 19th International Congress of Metrology (CIM2019), edited by Sandrine Gazal. Les Ulis, France: EDP Sciences, 2019. http://dx.doi.org/10.1051/metrology/201916001.

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In 2012, the Joint Committee for Guides in Metrology (JCGM) published novel guidance on the consideration of measurement uncertainty for decision-making in conformity assessment (JCGM 106:2012). The two situations of making a wrong decision are considered: the risk of accepting a non-conforming item, denoted as the customer risk, and the risk of rejecting a conforming item, denoted as the producer risk. In 2017, the revision of ISO 17025 obliged calibration and testing laboratories to “document the decision rule employed, taking into account the level of risk (such as false accept and false reject and statistical assumptions) associated with the decision rule employed, and apply the decision rule” in the context of the decision made about the conformity of an item. However, JCGM 106:2012 can in some cases be perceived as quite difficult to apply for non-statisticians as it mainly relies on calculations involving probability distributions. In order to facilitate uptake of the methodology of JCGM 106:2012, EURAMET is funding the project EMPIR 17SIP05 “CASoft” (2018 – 2020), involving the National Measurement Institutes from France, Sweden and the UK. The objective is to make the methodology accessible to organisations involved in decision-making in conformity assessment: calibration and testing laboratories, industrialists and regulation authorities. Where the customer or producer are concerned, there are two kinds of risks arising from measurement uncertainty: specific risk which concerns the risk of an incorrect decision for a particular item and global risk which is the risk of an incorrect decision for any item chosen at random. Both kinds of risk may involve prior information, taken into account through a so-called prior probability distribution, introducing the concept of a Bayesian evaluation of the risks. If a calibration and testing laboratory performing the measurement has difficulty accessing prior information, it is likely that the industrialist in control of production processes will have some idea of the quality of the items produced. In this paper, the two problems of estimating the specific and global risks are addressed. The consideration of prior information is also discussed through a practical example as well as the use of software implementing the methodology, which will be made publically available at the end of the project.

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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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