Relevant bibliographies by topics / Bayesian statistical decision theory

Academic literature on the topic 'Bayesian statistical decision theory'

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Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "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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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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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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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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Reinhardt, Howard E. "Statistical Decision Theory and Bayesian Analysis. Second Edition (James O. Berger)." SIAM Review 29, no. 3 (September 1987): 487–89. http://dx.doi.org/10.1137/1029095.

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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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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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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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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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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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Dissertations / Theses on the topic "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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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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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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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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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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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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Yeo, Yeongseo. "Bayesian scientific methodology : a naturalistic approach /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3074459.

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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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Pei, Xin, and 裴欣. "Bayesian approach to road safety analyses." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46591989.

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Books on the topic "Bayesian statistical decision theory"

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O, Berger James, ed. Statistical decision theory and Bayesian analysis. 2nd ed. New York: Springer-Verlag, 1985.

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Berger, James O. Statistical Decision Theory and Bayesian Analysis. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4757-4286-2.

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Berger, James O. Statistical decision theory and Bayesian analysis. 2nd ed. New York: Springer-Verlag, 1985.

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Lid, Hjort Nils, ed. Bayesian nonparametrics. New York: Cambridge University Press, 2009.

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John, Stutz, Cheeseman Peter, and Ames Research Center. Artificial Intelligence Research Branch., eds. Bayesian classification theory. Moffett Field, CA: NASA Ames Research Center, Artificial Intelligence Research Branch, 1991.

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Guttman, Irwin. Bayesian power. Toronto: University of Toronto, Dept. of Statistics, 1986.

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

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Bansal, Ashok K. Bayesian parametric inference. Oxford, U.K: Alpha Science International Ltd., 2007.

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Book chapters on the topic "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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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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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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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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Ghosh, Malay. "On Some Bayesian Solutions of the Neyman-Scott Problem." In Statistical Decision Theory and Related Topics V, 267–76. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2618-5_20.

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Conference papers on the topic "Bayesian statistical decision theory"

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Spross, J., S. Hintze, and S. Larsson. "Optimization of LCC for Soil Improvement Using Bayesian Statistical Decision Theory." In 8th International Symposium on Reliability Engineering and Risk Management. Singapore: Research Publishing Services, 2022. http://dx.doi.org/10.3850/978-981-18-5184-1_ms-13-031-cd.

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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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"The Value of Subjective Information: An Empirical Assessment." In NCSL International Workshop & Symposium. NCSL International, 2018. http://dx.doi.org/10.51843/wsproceedings.2018.16.

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Metrology engineers want technically correct answers. Managers want to make decisions that trade off cost against product value. Calibration personnel want their work to count. Calibration intervals drive measurement reliability, the calibration budget, and the value of every calibration. We affect the value of our entire calibration program when we decide how often to calibrate. Unfortunately, we don’t always have enough historical calibration results data to predict the best calibration interval with a high degree of confidence. Although Bayesian statistical theory provides a method for including independent data sources to supplement calibration results data, limited empirical evidence exists to assess how well Bayesian statistics predicts measurement reliability. The literature has no example that measures how well subjective information estimates measurement reliability.
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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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Kumar, Deepika Saxena, and Dr Raj Thaneeghaivel V. "USING AND INVESTIGATING BIG DATA MINING IN CLINICAL MEDICINE." In Computing for Sustainable Innovation: Shaping Tomorrow’s World. Innovative Research Publication, 2024. http://dx.doi.org/10.55524/csistw.2024.12.1.69.

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To examine big data mining ideas,technology, and applications in clinical medicine. PubMed and the Chinese Hospital Knowledge Database were used to find English and Chinese publications from 1975 to 2015 that discussed big datamining ideas, technologies,and practical applications in clinical medicine. Selection of Studies: original research on the theory and technology of big data mining and its uses in the medical industry. Fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis are some of the fundamental theories and technologies of big data mining that were covered in this review. Big data mining's use in clinical medicine was examined in the Big data mining has the potential to play an important role in clinical medicine.
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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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Varady, C., J. Tenório, E. Silva, E. Lima Junior, J. Santos, R. Dias, and F. Cutrim. "Bayesian-Based Approach in Soil Characterization for Top-Hole Design." In Offshore Technology Conference. OTC, 2024. http://dx.doi.org/10.4043/35039-ms.

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Abstract This paper addresses Bayesian-based data-driven site characterization methods for estimating soil parameters used in top-hole casing design. Different models are applied to datasets of piezocone tests (CPTu) conducted in Brazilian fields and their performance is compared to some characterization techniques currently employed by the oil and gas industry. Regression models are crucial for soil characterization for top-hole casing design. Data-driven methods consist of a powerful tool for this purpose, allowing handling uncertainties originating from soil variability. This paper addresses machine learning models devoted to sparse data, namely Geotechnical lasso (Glasso) and Gaussian Process Regression (GPR) from a Bayesian perspective considering prior knowledge of site information. This approach provides statistical information on soil parameters like undrained shear strength, supporting structural analysis of wellhead systems, and conductor/surface casing strings. Datasets were collected from in-situ CPTu tests conducted in the Campos basins, in eastern Brazil. The first case study addressed primary CPTu data, including cone tip resistance, friction sleeve, and total pore pressure, to characterize undrained shear resistance as a random variable. In this context, the parameter was modeled using Phoon´s modified Bartlett test to calculate sample sizes that ensure stationarity. This approach presented relevant results in assessing the probability of failure for conductor and surface casing design based on the operator´s internal design criteria. As these applications, more robust techniques were used to improve data characterization. Glasso and GPR models are used to model parameter tendencies and evaluation of the random data. These methods differ in prior probability density functions adopted - Laplace and Gaussian, respectively - and they are compared with regression techniques widely used in design practice. All the models were trained to estimate undrained shear resistance. Preliminary results confirm that the technique improves the characterization of soil strata and undrained shear strength, with a beneficial effect on the analysis of offshore top-hole structural design cases. Some metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) indicate that GPR outperforms other methods. This is an innovative methodology applied in real-case scenarios with data ceded from the partner operator. The formulation evaluates uncertainties associated with the spatial heterogeneity of the material, continuously improving robustness with each new project data. This enables a better understanding of soil behavior in specific oilfields and can assist the decision-making process in well design, improving operational safety. Furthermore, the results of the statistical modeling support reliability-based analysis to deliver probability-based indicators for well integrity design.
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Davis, Brad, Gregory Langone, and Nicholas Reisweber. "Sensitivity Analysis and Bayesian Calibration of a Holmquist-Johnson-Cook Material Model for Cellular Concrete Subjected to Impact Loading." In ASME 2022 Verification, Validation, and Uncertainty Quantification Symposium. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/vvs2022-86800.

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Abstract Periodic updates to small caliber weapon systems and projectiles used in military and law enforcement have resulted in consistently increasing material penetration capabilities. With each new generation, ballistics technology outpaces the lifecycle replacement of live-fire training facilities. For this reason, it is necessary to develop and maintain constitutive material models for use in analyzing the effects new threats will have on existing facilities and for designing new training facilities using numerical methods. This project utilizes material testing data to characterize cellular concretes used in the construction of live-fire training facilities with a 13-parameter Holmquist-Johnson-Cook (HJC) concrete constitutive model. Various statistical tools are used in this analysis to successfully describe the importance of each model parameter and quantify their uncertainty. First, Bayesian linear regression was used to calibrate the parameters in the strength and pressure components of the HJC material model given testing data of cellular concrete. These uncertain parameters were then used to construct computer simulations of penetration and perforation experiments that were previously conducted by Collard and Lanham. Then, Latin Hypercube Sampling of the parameter space was used to generate training data for a Gaussian Process surrogate model of the computer simulation. Using the surrogate model, a global variance-based sensitivity analysis of the material model was completed by computing main and total effect Sobol indices. Finally, a Bayesian calibration of the computer simulation based on the physical experiments was conducted to fully characterize the stochastic behavior of the material subjected to perforation impacts. These approaches can be used to inform decision makers about the potential risk associated with existing facilities and by designers of future live fire training facilities.
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Aileni, Raluca maria. "HEALTHCARE PREDICTIVE MODELS BASED ON BIG DATA FUSION FROM BIOMEDICAL SENSORS." In eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-046.

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The paper presents a method for analyzing data from sensors and developing the predictive models based on learning methods. There are some methods, described on scientific literature, such as statistical methods (linear regression, logistic regression, and Bayesian models), advanced methods based on machine learning and data mining (decision trees and artificial neural networks) and survival models. All of these methods are intended to discover the correlation and covariance between biomedical parameters. This paper presents the decision tree method for predictive health modeling based on machine learning and data mining. Based on this method used can be developed a decision support system for healthcare. Machine learning is used in healthcare predictive modeling for learning to recognize complex patterns within big data received from biomedical sensors. The sensors data fusion refers to the usage of the sensors wireless network and data fusion on the same level (for similar sensors - e. g. temperature sensors) and on different levels (different sensors category - pulse, breath, temperature, moisture sensors) for developing the decision systems. Big data concept is familiar for medical sciences (genomics, biomedical research) and also for physical sciences (meteorology, physics and chemistry), financial institutions (banking and capital markets) and government (defense). For predictive models in clinical analysis is important to establish the time steps discretization of occurrence of a particular event (critical state required continuous monitoring) for observe the impact of the correlated values for biomedical parameters. These aspects presented are useful for healthcare learning about correlation between diseases and biomedical parameters.
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Reports on the topic "Bayesian statistical decision theory"

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Darling, Arthur H., and William J. Vaughan. The Optimal Sample Size for Contingent Valuation Surveys: Applications to Project Analysis. Inter-American Development Bank, April 2000. http://dx.doi.org/10.18235/0008824.

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One of the first questions that has to be answered in the survey design process is "How many subjects should be interviewed?" The answer can have significant implications for the cost of project preparation, since in Latin America and the Caribbean costs per interview can range from US$20 to US$100. Traditionally, the sample size question has been answered in an unsatisfactory way by either dividing an exogenously fixed survey budget by the cost per interview or by employing some variant of a standard statistical tolerance interval formula. The answer is not to be found in the environmental economics literature. But, it can be developed by adapting a Bayesian decision analysis approach from business statistics. The paper explains and illustrates, with a worked example, the rationale for and mechanics of a sequential Bayesian optimization technique, which is only applicable when there is some monetary payoff to alternative courses of action that can be linked to the sample data.
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Roberts, Nancy A. Using Bayesian Networks and Decision Theory to Model Physical Security. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada411379.

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3

Russell, Stuart, Shankar Sastry, Rene Vidal, I.-Jeng Wang, Andreas Terzis, Yanif Ahmad, Avi Pfeffer, and Edwin Chong. Open-Universe Theory for Bayesian Inference, Decision, and Sensing (OUTBIDS). Fort Belvoir, VA: Defense Technical Information Center, January 2014. http://dx.doi.org/10.21236/ada598009.

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4

Shao, Jun. Monte Carlo Approximations in Bayesian Decision Theory. Part 3. Limiting Behavior of Monte Carlo Approximations. Fort Belvoir, VA: Defense Technical Information Center, December 1988. http://dx.doi.org/10.21236/ada204173.

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5

Kanno, Yoichiro, Dan Preston, Yoichiro Kanno, and Dan Preston. Fisheries inventories at Rocky Mountain National Park to inform cutthroat trout conservation and recreational angling decision post-fire. National Park Service, 2024. http://dx.doi.org/10.36967/2304877.

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The Cameron Peak Fire and East Troublesome Fire of 2020 were the two largest wildfires in Colorado history. They burned approximately 9% of the Rocky Mountain National Park, raising a concern for trout populations that currently support recreational fishing and success of on-going and future efforts to conserve native trout populations. We inventoried habitat characteristics and biological communities at 19 sites in summer of 2021 and a subset of 11 sites in summer of 2022 to characterize wildfire impacts on aquatic resources, with the focus on characterizing trout population responses. There was much site-to-site variation in the trout population responses, but when averaged across sites using Bayesian hierarchical models, trout abundance significantly decreased in 2021 relative to pre-fire abundance, and the decrease was more evident in smaller trout (75-125 mm total length) than in larger trout (> 125 mm). From 2021 to 2022, trout abundance generally increased, although the increase was statistically significant only in small trout. Although pre-fire data were lacking for benthic macroinvertebrates, their abundance and composition was comparable between burned sites and those outside the fire perimeter, indicating that prey availability to trout was not limited. Our results show that trout abundance decreased post-fire, but trout populations were not eradicated and are likely in a recovery phase. These data cannot be used to argue for stocking trout to sustain recreational fisheries or discontinuing native trout conservation actions including the Poudre Headwaters Project.
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Juden, Matthew, Tichaona Mapuwei, Till Tietz, Rachel Sarguta, Lily Medina, Audrey Prost, Macartan Humphreys, et al. Process Outcome Integration with Theory (POInT): academic report. Centre for Excellence and Development Impact and Learning (CEDIL), March 2023. http://dx.doi.org/10.51744/crpp5.

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This paper describes the development and testing of a novel approach to evaluating development interventions – the POInT approach. The authors used Bayesian causal modelling to integrate process and outcome data to generate insights about all aspects of the theory of change, including outcomes, mechanisms, mediators and moderators. They partnered with two teams who had evaluated or were evaluating complex development interventions: The UPAVAN team had evaluated a nutrition-sensitive agriculture intervention in Odisha, India, and the DIG team was in the process of evaluating a disability-inclusive poverty graduation intervention in Uganda. The partner teams’ theory of change were adapted into a formal causal model, depicted as a directed acyclic graph (DAG). The DAG was specified in the statistical software R, using the CausalQueries package, having extended the package to handle large models. Using a novel prior elicitation strategy to elicit beliefs over many more parameters than has previously been possible, the partner teams’ beliefs about the nature and strength of causal links in the causal model (priors) were elicited and combined into a single set of shared prior beliefs. The model was updated on data alone as well as on data plus priors to generate posterior models under different assumptions. Finally, the prior and posterior models were queried to learn about estimates of interest, and the relative role of prior beliefs and data in the combined analysis.
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Tian, Cong, Jianlong Shu, Wenhui Shao, Zhengxin Zhou, Huayang Guo, and Jingang Wang. The efficacy and safety of IL Inhibitors, TNF-α Inhibitors, and JAK Inhibitor on ankylosing spondylitis: A Bayesian network meta-analysis of a “randomized, double-blind, placebo-controlled” trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, September 2022. http://dx.doi.org/10.37766/inplasy2022.9.0117.

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Review question / Objective: In this study, we conducted a Bayesian network meta-analysis to evaluate the efficacy and safety of interleukin (IL) inhibitors, tumor necrosis factor-alpha (TNF-α) inhibitors, and Janus kinase (JAK) inhibitors on ankylosing spondylitis (AS).The purpose of this study is to compare the effectiveness and safety of different interventions for treating AS to provide insights into the decision-making in clinicalpractice. Condition being studied: Ankylosing spondylitis. Based on the Bayesian hierarchical model, we conducted a network meta-analysis using the gemtc package in R software (version 4.1.3) and Stata software (version 15.1). Cong Tian and Jianlong Shu contributed to the conception and design of the study and supervised the tweet classification. All authors drafted the manuscript. Wenhui Shao, Zhengxin Zhou, Huayang Guo and Jingang Wang contributed to data management and tweet classification. Cong Tian, Jianlong Shu and Zhengxin Zhou performed the statistical analysis. Cong Tian, Jianlong Shu, Wenhui Shao and Zhengxin Zhou reviewed the manuscript.
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8

Heckman, Stuart. Understanding insurance decisions: A review of risk management decision making, risk literacy, and racial/ethnic differences. Center for Insurance Policy and Research, January 2024. http://dx.doi.org/10.52227/26712.2024.

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The racial/ethnic wealth gap is a stunning feature of U.S. household finances. Although the causes of the gap are complex, it is important that researchers investigate disparities between racial/ethnic groups in household financial management areas. We posit that first understanding insurance decisions as a critical component of overall household financial management is an important avenue for further understanding factors that may perpetuate or reduce the racial wealth gap. Moreover, risk management, including the purchase and use of insurance products, is a key yet challenging area for household financial management. Therefore, this literature review focuses on research relevant to three main questions: 1) How do consumers make risk management decisions? 2) What key skills are required to make risk management decisions (with a focus on literacy and numeracy skills)? 3) Do these skills vary between racial/ethnic groups? Regarding the first question, we find that consumers are prone to errors when making decisions involving risk, but research shows that decisions can be improved. Skilled Decision Theory (SDT) highlights that cognitive ability plays less of a central role in decision-making and that decision-making is more of an acquired skill. Consequently, learning comprehension and confidence play a crucial role in the decision-making process. In terms of the second question and the skills needed to make appropriate risk management decisions, the literature suggests that insurance literacy, not necessarily financial literacy, as well as numeracy skills are likely to be critical prerequisites to good insurance choices. In particular, the importance of statistical numeracy in decision-making cannot be overstated. Finally for our third question, our review indicates that there is a relatively limited number of available studies focusing on racial/ethnic differences in risk management decisions and skills. While some studies find differences between racial/ethnic groups in various measures of financial literacy, the findings are overall mixed and, therefore, inconclusive. Researchers should verify if there are, in fact, differences or if the differences are due to other factors that vary by racial/ethnic category.
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