Advances in High-Order Predictive Modeling av Dan Gabriel (University of South Carolina) Cacuci

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<p>Continuing the author¿s previous work on modeling, this book presents the most recent advances in high-order predictive modeling. The author begins with the mathematical framework of the 2<sup>nd</sup>-BERRU-PM methodology, an acronym that designates the ¿second-order best-estimate with reduced uncertainties (2<sup>nd</sup>-BERRU) predictive modeling (PM).¿ The 2<sup>nd</sup>-BERRU-PM methodology is fundamentally anchored in physics-based principles stemming from thermodynamics (maximum entropy principle) and information theory, being formulated in the most inclusive possible phase-space, namely the combined phase-space of computed and measured parameters and responses.</p><p>The 2<sup>nd</sup>-BERRU-PM methodology provides second-order output (means and variances) but can incorporate, as input, arbitrarily high-order sensitivities of responses with respect to model parameters, as well as arbitrarily high-order moments of the initial distribution of uncertain model parameters, in or