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Articoli di riviste sul tema "Bayesian statistical decision theory"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 30 luglio 2024

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1

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 glo

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2

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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3

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 Servic

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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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 th

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12

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 purp

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13

Majeed, Nasir, Amjad Hilal, and Tabinda Rani. "UNDERSTANDING BAYES’ THEOREM AND ITS APPLICATION IN JUDICIAL DECISION MAKING." Pakistan Journal of Social Research 05, no. 02 (June 30, 2023): 449–57. http://dx.doi.org/10.52567/pjsr.v5i02.1096.

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The Bayes’ theorem is a mathematical formula to gauge and describe the probability of an event by employing prior knowledge and evidence relevant to the event. The objective of the present study was to understand the Bayesian theorem and its application in judicial trials by deploying doctrinal research methodology. After consulting authoritative writings of prominent researchers and judicial decisions, study found that the Bayesian Probability in legal context used in odds version, likelihood ratio and in the form of Bayesian networks. The study also found that the application of the theorem

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14

Johnson, Richard A., and Abderrahmane Mouhab. "A Bayesian Decision Theory Approach to Classification Problems." Journal of Multivariate Analysis 56, no. 2 (February 1996): 232–44. http://dx.doi.org/10.1006/jmva.1996.0012.

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15

Abraham, Christophe, and Jean-Pierre Daurès. "Robust Bayesian decision theory applied to optimal dosage." Statistics in Medicine 23, no. 7 (2004): 1055–73. http://dx.doi.org/10.1002/sim.1690.

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16

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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17

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 probabi

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18

Alho, Juha M., and Jyrki Kangas. "Analyzing Uncertainties in Experts' Opinions of Forest Plan Performance." Forest Science 43, no. 4 (November 1, 1997): 521–28. http://dx.doi.org/10.1093/forestscience/43.4.521.

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Abstract Multi-objective forestry requires new decision support systems to aid the forest owner and foresters in the planning of future treatment schedules. The analytic hierarchy process (AHP), based on pairwise comparison data and Saaty's eigenvector method, is one technique that has been proposed to make such qualitatively different objectives as income from timber sales and scenic beauty of forest landscape commensurable. A weak point of the methodology has been the lack of a statistical theory behind it. We have earlier shown how classical regression techniques can be used to provide a st

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19

Galvani, Marta, Chiara Bardelli, Silvia Figini, and Pietro Muliere. "A Bayesian Nonparametric Learning Approach to Ensemble Models Using the Proper Bayesian Bootstrap." Algorithms 14, no. 1 (January 3, 2021): 11. http://dx.doi.org/10.3390/a14010011.

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Bootstrap resampling techniques, introduced by Efron and Rubin, can be presented in a general Bayesian framework, approximating the statistical distribution of a statistical functional ϕ(F), where F is a random distribution function. Efron’s and Rubin’s bootstrap procedures can be extended, introducing an informative prior through the Proper Bayesian bootstrap. In this paper different bootstrap techniques are used and compared in predictive classification and regression models based on ensemble approaches, i.e., bagging models involving decision trees. Proper Bayesian bootstrap, proposed by Mu

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20

Gelman, Andrew. "Some Class-Participation Demonstrations for Decision Theory and Bayesian Statistics." American Statistician 52, no. 2 (May 1998): 167. http://dx.doi.org/10.2307/2685476.

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21

Gelman, Andrew. "Some Class-Participation Demonstrations for Decision Theory and Bayesian Statistics." American Statistician 52, no. 2 (May 1998): 167–74. http://dx.doi.org/10.1080/00031305.1998.10480557.

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22

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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23

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 Baye

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24

North, D. W. "Analysis of Uncertainty and Reaching Broad Conclusions." Journal of the American College of Toxicology 7, no. 5 (September 1988): 583–90. http://dx.doi.org/10.3109/10915818809019535.

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Probability theory can provide a general way of reasoning about uncertainty, even when data are sparse or absent. The idea that probabilities can represent judgment is a basic principle for decision analysis and for the Bayesian school of statistics. The use of judgmental probabilities and Bayesian statistical methods for the analysis of toxicological data appears to be promising in reaching broad conclusions for policy and for research planning. Illustrative examples are given using quantal dose-response data from carcinogenicity bioassays for two chemicals, perchloroethylene and alachlor.

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25

Shao, Jun, and Shein-Chung Chow. "Constructing Release Targets for Drug Products: A Bayesian Decision Theory Approach." Applied Statistics 40, no. 3 (1991): 381. http://dx.doi.org/10.2307/2347518.

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26

Fang, Chih-Chiang, Chin-Chia Hsu, and Je-Hung Liu. "Bayesian Statistical Method Enhance the Decision-Making for Imperfect Preventive Maintenance with a Hybrid Competing Failure Mode." Axioms 11, no. 12 (December 15, 2022): 734. http://dx.doi.org/10.3390/axioms11120734.

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The study aims to provide a Bayesian statistical method with natural conjugate for facilities’ preventive maintenance scheduling related to the hybrid competing failure mode. An effective preventive maintenance strategy not only can improve a system’s health condition but also can increase a system’s efficiency, and therefore a firm needs to make an appropriate strategy for increasing the utilization of a system with reasonable costs. In the last decades, preventive maintenance issues of deteriorating systems have been studied in the related literature, and hundreds of maintenance/replacement

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27

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

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28

Lee, Michael D. "Bayesian methods for analyzing true-and-error models." Judgment and Decision Making 13, no. 6 (November 2018): 622–35. http://dx.doi.org/10.1017/s193029750000663x.

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AbstractBirnbaum and Quispe-Torreblanca (2018) evaluated a set of six models developed under true-and-error theory against data in which people made choices in repeated gambles. They concluded the three models based on expected utility theory were inadequate accounts of the behavioral data, and argued in favor of the simplest of the remaining three more general models. To reach these conclusions, they used non-Bayesian statistical methods: frequentist point estimation of parameters, bootstrapped confidence intervals of parameters, and null hypothesis significance testing of models. We address

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29

Brunier, Hazel C., and John Whitehead. "Sample sizes for phase ii clinical trials derived from Bayesian decision theory." Statistics in Medicine 13, no. 23-24 (December 15, 1994): 2493–502. http://dx.doi.org/10.1002/sim.4780132312.

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30

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 dea

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31

Lepora, Nathan F., and Kevin N. Gurney. "The Basal Ganglia Optimize Decision Making over General Perceptual Hypotheses." Neural Computation 24, no. 11 (November 2012): 2924–45. http://dx.doi.org/10.1162/neco_a_00360.

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The basal ganglia are a subcortical group of interconnected nuclei involved in mediating action selection within cortex. A recent proposal is that this selection leads to optimal decision making over multiple alternatives because the basal ganglia anatomy maps onto a network implementation of an optimal statistical method for hypothesis testing, assuming that cortical activity encodes evidence for constrained gaussian-distributed alternatives. This letter demonstrates that this model of the basal ganglia extends naturally to encompass general Bayesian sequential analysis over arbitrary probabi

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32

Thapa, Samudrajit, Seongyu Park, Yeongjin Kim, Jae-Hyung Jeon, Ralf Metzler, and Michael A. Lomholt. "Bayesian inference of scaled versus fractional Brownian motion." Journal of Physics A: Mathematical and Theoretical 55, no. 19 (April 12, 2022): 194003. http://dx.doi.org/10.1088/1751-8121/ac60e7.

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Abstract We present a Bayesian inference scheme for scaled Brownian motion, and investigate its performance on synthetic data for parameter estimation and model selection in a combined inference with fractional Brownian motion. We include the possibility of measurement noise in both models. We find that for trajectories of a few hundred time points the procedure is able to resolve well the true model and parameters. Using the prior of the synthetic data generation process also for the inference, the approach is optimal based on decision theory. We include a comparison with inference using a pr

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33

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 bas

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34

Charlton, Julie A., Wiktor F. Młynarski, Yoon H. Bai, Ann M. Hermundstad, and Robbe L. T. Goris. "Environmental dynamics shape perceptual decision bias." PLOS Computational Biology 19, no. 6 (June 8, 2023): e1011104. http://dx.doi.org/10.1371/journal.pcbi.1011104.

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To interpret the sensory environment, the brain combines ambiguous sensory measurements with knowledge that reflects context-specific prior experience. But environmental contexts can change abruptly and unpredictably, resulting in uncertainty about the current context. Here we address two questions: how should context-specific prior knowledge optimally guide the interpretation of sensory stimuli in changing environments, and do human decision-making strategies resemble this optimum? We probe these questions with a task in which subjects report the orientation of ambiguous visual stimuli that w

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Yang, Jen-Jen, Yen-Ching Chuang, Huai-Wei Lo, and Ting-I. Lee. "A Two-Stage MCDM Model for Exploring the Influential Relationships of Sustainable Sports Tourism Criteria in Taichung City." International Journal of Environmental Research and Public Health 17, no. 7 (March 30, 2020): 2319. http://dx.doi.org/10.3390/ijerph17072319.

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Many countries advocate sports for all to cultivate people’s interest in sports. In cities, cross-industry alliances between sports and tourism are one of the common practices. The following two important issues need to be discussed, namely, what factors should be paid attention to in the development of sports tourism, and what are the mutual influential relationships among these factors. This study proposes a novel two-stage multi-criteria decision-making (MCDM) model to incorporate the concept of sustainable development into sports tourism. First, the Bayesian best–worst method (Bayesian BWM

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Scales, John A., and Luis Tenorio. "Prior information and uncertainty in inverse problems." GEOPHYSICS 66, no. 2 (March 2001): 389–97. http://dx.doi.org/10.1190/1.1444930.

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Solving any inverse problem requires understanding the uncertainties in the data to know what it means to fit the data. We also need methods to incorporate data‐independent prior information to eliminate unreasonable models that fit the data. Both of these issues involve subtle choices that may significantly influence the results of inverse calculations. The specification of prior information is especially controversial. How does one quantify information? What does it mean to know something about a parameter a priori? In this tutorial we discuss Bayesian and frequentist methodologies that can

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Huang, Yi Hu, Jin Li Wang, and Xi Mei Jia. "Research of Soccer Robot Target Tracking Algorithm Based on Improved CAMShift." Advanced Materials Research 221 (March 2011): 610–14. http://dx.doi.org/10.4028/www.scientific.net/amr.221.610.

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According to the vision needs of robot soccer and CAMShift tracking inefficient in dynamic background, a new tracking algorithm is brought forward to improve the CAMShift in this paper. A real-time updating background model is build, by traversing the search area for all target pixels to statistic and calculate the color probability distribution of the color target, statistical principles and minimum error rate of Bayesian decision theory are used to achieve a more accurate distinction between the target and the background. By comparing with the CAMShift, the new algorithm provides a better ro

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Chow, Siu L. "The null-hypothesis significance-test procedure is still warranted." Behavioral and Brain Sciences 21, no. 2 (April 1998): 228–35. http://dx.doi.org/10.1017/s0140525x98591169.

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Entertaining diverse assumptions about empirical research, commentators give a wide range of verdicts on the NHSTP defence in Statistical significance. The null-hypothesis significance-test procedure (NHSTP) is defended in a framework in which deductive and inductive rules are deployed in theory corroboration in the spirit of Popper's Conjectures and refutations (1968b). The defensible hypothetico-deductive structure of the framework is used to make explicit the distinctions between (1) substantive and statistical hypotheses, (2) statistical alternative and conceptual alternative hypotheses, a

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Stallard, Nigel. "Sample Size Determination for Phase II Clinical Trials Based on Bayesian Decision Theory." Biometrics 54, no. 1 (March 1998): 279. http://dx.doi.org/10.2307/2534014.

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Brown, B. "The choice of variables in multivariate regression: a non-conjugate Bayesian decision theory approach." Biometrika 86, no. 3 (September 1, 1999): 635–48. http://dx.doi.org/10.1093/biomet/86.3.635.

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Vasiliev, O. V., E. S. Boyarenko, and K. I. Galaeva. "Substantiation of source data on the parametric algorithms for the classification of weather hazards." Civil Aviation High Technologies 26, no. 6 (December 25, 2023): 8–21. http://dx.doi.org/10.26467/2079-0619-2023-26-6-8-21.

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The meteorological situation is one of the decisive factors determining the safety and frequency of civil aviation flights. Weather hazards (WH), associated with cumulonimbus clouds, such as a heavy shower, thunderstorm, hail, combined with high atmosphere turbulence, quite often lead to aviation events and even accidents. Currently, a domestic weather radar system of the near airfield zone (WR) “Monocle” has been developed and successfully operated. The criteria for the classification of meteorological phenomena (MP), used in the WR, have been developed individually for each phenomenon and ha

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Mukha, V. S., and N. F. Kako. "Integrals and integral transformations related to the vector Gaussian distribution." Proceedings of the National Academy of Sciences of Belarus. Physics and Mathematics Series 55, no. 4 (January 7, 2020): 457–66. http://dx.doi.org/10.29235/1561-2430-2019-55-4-457-466.

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This paper is dedicated to the integrals and integral transformations related to the probability density function of the vector Gaussian distribution and arising in probability applications. Herein, we present three integrals that permit to calculate the moments of the multivariate Gaussian distribution. Moreover, the total probability formula and Bayes formula for the vector Gaussian distribution are given. The obtained results are proven. The deduction of the integrals is performed on the basis of the Gauss elimination method. The total probability formula and Bayes formula are obtained on t

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Rue, Håvard, and Anne Randi Syversveen. "Bayesian object recognition with baddeley's delta loss." Advances in Applied Probability 30, no. 01 (March 1998): 64–84. http://dx.doi.org/10.1017/s0001867800008089.

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A common problem in Bayesian object recognition using marked point process models is to produce a point estimate of the true underlying object configuration: the number of objects and the size, location and shape of each object. We use decision theory and the concept of loss functions to design a more reasonable estimator for this purpose, rather than using the common zero-one loss corresponding to the maximum a posteriori estimator. We propose to use the squared Δ-metric of Baddeley (1992) as our loss function and demonstrate that the corresponding optimal Bayesian estimator can be well appro

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Domanov, Aleksey. "The Basics of Bayesian Approach to Quantitative Analysis (at the Example of Euroscepticism)." Political Science (RU), no. 1 (2021): 301–21. http://dx.doi.org/10.31249/poln/2021.01.13.

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This article attempts to identify the main assumptions, prerequisites and techniques of the methods developed by some modern statisticians on the basis of T. Bayes' theorem for the purposes of social variables interactions assessment. The author underlined several advantages of the given approach as compared to more traditional quantitative methods and highlighted key research areas subject to evaluation by Bayesian estimates. First of all, this approach is compatible with game and decision theory, event analysis, hidden Markov chains, prediction using neural networks and other predictive algo

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Nemykin, O. I. "ALGORITHM FOR SELECTION OF LAUNCH ELEMENTS IN THE PRESENCE OF A PRIORI INFORMATION ABOUT ITS COMPOSITION AND STRUCTURE." Issues of radio electronics, no. 3 (March 20, 2018): 114–19. http://dx.doi.org/10.21778/2218-5453-2018-3-114-119.

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Traditional methods of the theory of statistical solutions are developed for cases of making single-valued two-alternative or multialternative solutions about the class of an object. Assuming the possibility of ambiguous multi-alternative (in the case of solving the problem of selection of space objects of three-alternative) decisions on the classification of of space objects at the stages of the selection process, a modification of the traditional statistical decision making algorithm is required. Such a modification of the algorithm can be carried out by appropriate selection of the loss fun

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Braun, Marie. "The Effectiveness of Actuarial Models in Predicting Insurance Claims in Germany." Journal of Statistics and Actuarial Research 8, no. 2 (July 5, 2024): 42–52. http://dx.doi.org/10.47604/jsar.2763.

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Abstract (sommario):

Purpose: The aim of the study was to analyze the effectiveness of actuarial models in predicting insurance claims in Germany. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Actuarial models in Germany effectively predict insurance cl

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47

Maiworm, Mario, Peter König, and Brigitte Röder. "Integrative Processing of Perception and Reward in an Auditory Localization Paradigm." Experimental Psychology 58, no. 3 (November 1, 2011): 217–26. http://dx.doi.org/10.1027/1618-3169/a000088.

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Abstract (sommario):

Under natural conditions, human beings routinely have to choose among multiple alternatives which are associated with specific outcomes of varying desirability. Typically, decisions are based upon the processing of perceptual input, which introduces additional noise to the system. Bayesian decision theory (BDT) allows us to formalize decision under risk and to predict statistically optimal choice behavior. In the present study, human observers performed a classification task characterized by an extensive amount of perceptual uncertainty (auditory localization). In addition, a spatial reward fu

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48

FELGAER, PABLO, PAOLA BRITOS, and RAMÓN GARCÍA-MARTÍNEZ. "PREDICTION IN HEALTH DOMAIN USING BAYESIAN NETWORKS OPTIMIZATION BASED ON INDUCTION LEARNING TECHNIQUES." International Journal of Modern Physics C 17, no. 03 (March 2006): 447–55. http://dx.doi.org/10.1142/s0129183106008558.

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Abstract (sommario):

A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with

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49

STAUFFER, HOWARD B. "APPLICATION OF BAYESIAN STATISTICAL INFERENCE AND DECISION THEORY TO A FUNDAMENTAL PROBLEM IN NATURAL RESOURCE SCIENCE: THE ADAPTIVE MANAGEMENT OF AN ENDANGERED SPECIES." Natural Resource Modeling 21, no. 2 (April 29, 2008): 264–84. http://dx.doi.org/10.1111/j.1939-7445.2008.00007.x.

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50

Kilpatrick, Zachary P., Jacob D. Davidson, and Ahmed El Hady. "Uncertainty drives deviations in normative foraging decision strategies." Journal of The Royal Society Interface 18, no. 180 (July 2021): 20210337. http://dx.doi.org/10.1098/rsif.2021.0337.

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Abstract (sommario):

Nearly all animals forage to acquire energy for survival through efficient search and resource harvesting. Patch exploitation is a canonical foraging behaviour, but there is a need for more tractable and understandable mathematical models describing how foragers deal with uncertainty. To provide such a treatment, we develop a normative theory of patch foraging decisions, proposing mechanisms by which foraging behaviours emerge in the face of uncertainty. Our model foragers statistically and sequentially infer patch resource yields using Bayesian updating based on their resource encounter histo

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