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Product Research - The Art and Science Behind Successful Product Launches (Hardcover, 2009 ed.)
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Product Research - The Art and Science Behind Successful Product Launches (Hardcover, 2009 ed.)
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7. 1. 1 Background Uncertainty can be considered as the lack of
adequate information to make a decision. It is important to
quantify uncertainties in mathematical models used for design and
optimization of nondeterministic engineering systems. In general, -
certainty can be broadly classi?ed into three types (Bae et al.
2004; Ha-Rok 2004; Klir and Wierman 1998; Oberkampf and Helton
2002; Sentz 2002). The ?rst one is aleatory uncertainty (also
referred to as stochastic uncertainty or inherent - certainty) - it
results from the fact that a system can behave in random ways. For
example, the failure of an engine can be modeled as an aleatory
uncertaintybecause the failure can occur at a random time. One
cannot predict exactly when the engine will fail even if a large
quantity of failure data is gathered (available). The second one is
epistemic uncertainty (also known as subjective uncertainty or
reducible - certainty) - it is the uncertainty of the outcome of
some random event due to lack of knowledge or information in any
phase or activity of the modeling process. By gaining information
about the system or environmental factors, one can reduce the
epistemic uncertainty. For example, a lack of experimental data to
characterize new materials and processes leads to epistemic
uncertainty.
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