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Thematische Bibliographien / Aposterior probability

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Konferenzberichte

Auswahl der wissenschaftlichen Literatur zum Thema „Aposterior probability“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 8. Februar 2022

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Zeitschriftenartikel zum Thema "Aposterior probability"

1

Imamura, Jun, Susumu Itoh und Toshio Utsunomiya. „Image Restoration by the Maximum Aposteriori Probability Estimation using Clustering.“ Journal of the Institute of Television Engineers of Japan 46, Nr. 5 (1992): 645–48. http://dx.doi.org/10.3169/itej1978.46.645.

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Eskov, Alexsander, Andrey Svistula und Alexey Dolmatov. „PARAMETERS OF FUEL JET EXTRACTION IN VIDEO“. TRANSPORT 20, Nr. 2 (20.04.2005): 62–65. http://dx.doi.org/10.3846/16484142.2005.9637997.

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The statistical rating of the image of a fuel jet allows to allocate the zones with homogeneous brightness, to define the distribution of masses and the concentration in a fuel jet and to look after their dynamics. In the research the coding with the usage of Byeses's definition of the aposteriory probability of the presence of the given brightness in the image is described. The bright zones of a fuel jet in a diesel engine will give to the conclusion how the fuel is distributed in the engine combustion camera.

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3

Groot, A. „Methods for estimating seedbed receptivity and for predicting seedling stocking and density in broadcast seeding“. Canadian Journal of Forest Research 18, Nr. 12 (01.12.1988): 1541–49. http://dx.doi.org/10.1139/x88-236.

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Two aposteriori and three multiple regression techniques were used to estimate the receptivity of 20 seedbed types in two trials of black spruce (Piceamariana (Mill.) B.S.P.) broadcast seeding in northern Ontario. None of the methods was a satisfactory substitute for experimental determination of receptivity by means of seed spots. Receptivity varied within seedbed types, and the β probability density function described the variability reasonably well. In the broadcast seeding trials, three probabilistic models were used to predict regeneration stocking from seeding rate, seedbed area, and seedbed receptivity. The model that took into account within-seedbed variability predicted seedling stocking with the lowest bias (−0.03).

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Dissertationen zum Thema "Aposterior probability"

1

Tomešová, Tereza. „Autonomní jednokanálový deinterleaving“. Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445470.

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This thesis deals with an autonomous single-channel deinterleaving. An autonomous single-channel deinterleaving is a separation of the received sequence of impulses from more than one emitter to sequences of impulses from one emitter without a human assistance. Methods used for deinterleaving could be divided into single-parameter and multiple-parameter methods according to the number of parameters used for separation. This thesis primarily deals with multi-parameter methods. As appropriate methods for an autonomous single-channel deinterleaving DBSCAN and variational bayes methods were chosen. Selected methods were adjusted for deinterleaving and implemented in programming language Python. Their efficiency is examined on simulated and real data.

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Mošna, Ondřej. „Bayesovský přístup v manažerském rozhodování“. Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-194697.

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This diploma thesis is about Bayesian approach in managerial decision making process. The goal is not only to quantify the principals used by managers during decision making in real situations but also the application of Bayesian methods on given examples. The mentioned principals are the probability updates after gaining a new information. In thesis are also described the computer systems which work with Bayesian calculations and a chosen system is described in detail. In a practical part of this thesis is demonstrated the use of Bayesian principals in real decision making situations -- there is demonstrated the use of Bayesian games, Bayesian networks (both classic and dynamic) and risk decision making process.

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Konferenzberichte zum Thema "Aposterior probability"

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Maneekut, Rachata, Shohei Beppu, Hidenori Takahashi und Takehiro Tsuritani. „Hybrid Probabilistic and Geometric Shaping for 64-QAM Optical Fiber Transmission with Maximum Aposterior Probability Detection“. In 2020 Opto-Electronics and Communications Conference (OECC). IEEE, 2020. http://dx.doi.org/10.1109/oecc48412.2020.9273678.

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Blanchard, Emmanuel, Corina Sandu und Adrian Sandu. „Comparison Between a Polynomial-Chaos-Based Bayesian Approach and a Polynomial-Chaos-Based EKF Approach for Parameter Estimation With Application to Vehicle Dynamics“. In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86402.

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Many parameters in mechanical systems cannot be measured physically with good accuracy, which results in parametric and external excitation uncertainties. This paper compares two new computational approaches for parameter estimation. The first approach is a polynomial-chaos based Bayesian approach in which maximum likelihood estimates are obtained by minimizing a cost function derived from the Bayesian theorem. The second one uses an Extended Kalman Filter (EKF) to obtain the polynomial chaos representation of the uncertain states and the uncertain parameters. The two methods are illustrated on a nonlinear four degree of freedom roll plane vehicle model, where an uncertain mass with an uncertain location is added on the roll bar. Both approaches can work with noisy measurements and yield results close to the actual values of the parameters, except when different combinations of uncertain parameters lead to essentially the same time response than the measured response. In that case, the aposteriori probability densities of the estimated parameters obtained with the EKF approach cannot be trusted. The Bayesian approach identifies that problem since the Bayesian cost function has an entire region of minima, and can use regularization techniques to yield most likely values in that region based on apriori knowledge.

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