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The whole idea is to consider the joint probability of both events, A and B, happening together (a man over 5'10" who plays in the NBA), and then perform some arithmetic on that relationship to provide a updated (posterior) estimate of a prior probability statement.By importing an FEA model and its mode shapes, or constructing an FEA model and solving for its modes prior to a modal test, this option helps you determine proper sensor and exciter locations for the test.This is because, often various analysts, would set their own boundaries, favouring their point of view, thus creating much confusion.One of such definitions would outline the Input-process-output system, as a structure, would be: "Systems thinking is the art and science of making reliable inferences about behaviour by developing an increasingly deep understanding of the understanding of the underlying structure" A system which has been created as a result of human interference, and is physically identifiable.The figures denote the cells of the table involved in each metric, the probability being the fraction of each figure that is shaded. P(A|B) = Bayesian inference derives the posterior probability as a consequence of two antecedents, a prior probability and a "likelihood function" derived from a statistical model for the observed data.Bayesian inference computes the posterior probability according to Bayes' theorem: – the posterior probability of a hypothesis is proportional to its prior probability (its inherent likeliness) and the newly acquired likelihood (its compatibility with the new observed evidence). If the evidence does not match up with a hypothesis, one should reject the hypothesis.

For example, if one does not know whether the newborn baby next door is a boy or a girl, the color of decorations on the crib in front of the door may support the hypothesis of one gender or the other; but if in front of that door, instead of the crib, a dog kennel is found, the posterior probability that the family next door gave birth to a dog remains small in spite of the "evidence", since one's prior belief in such a hypothesis was already extremely small.calibration and bias-correction, as well as different solution methods.Traditional approaches to calibration treat certain computer model parameters as fixed over the physical experiment, but unknown, and the objective is to infer values for the so-called calibration parameters that provide a better match between the physical and computer data.In other words, such inputs may be materials, human resources, money or information, transformed into outputs, such as consumables, services, new information or money.As a consequence, Input-Process-Output system becomes very vulnerable to misinterpretation.Our objective in this work is to provide a better understanding of the various model updating strategies that utilize mathematical means to update a computer model based on both physical and computer observations.

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