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.
We examine different model updating formulations, e.g.
In other words, such inputs may be materials, human resources, money or information, transformed into outputs, such as consumables, services, new information or money.
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.
New roads and itinearies are included in the updates.
Engineers see references to Bayesian Statistics everywhere.
Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law.
In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability". In the table, the values w, x, y and z give the relative weights of each corresponding condition and case.