Cycle-cutset sampling for Bayesian networks
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
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Nowadays, businesses consider that their methods are perfect, this means, that by having available a department of analysis and statistic control of the process, everything that the inspector or the inspection tools decides are considered to be correct, with not even a minimum of error involved. Yet, if they considered the principles of uncertainty of Heisenberg, in which he believes that the uncertainty associated to the observation, does not contradict the existence of laws that govern the behavior of the particles in the universe, not even the capacity of the scientists to discover those laws, which will be seen as precise predictions, which can be substituted by the calculations of probabilities. This investigation focuses on the study of CSP sampling plans for acceptance with Bayesian and Markovian revisions, in the processes of production in series and by lots, that support the quality activities and reduction of costs by inspection.