Titlebook: Decision Making: Uncertainty, Imperfection, Deliberation and Scalability; Tatiana V. Guy,Miroslav Kárny,David H. Wolpert Book 2015 The Edi

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https://doi.org/10.1007/978-3-8348-2185-0 causality, based on a vector autoregressive model, is one of the most popular methods for uncovering the temporal dependencies between time series, and so it can be used for estimating the causal relationships between the genes in the network. The application of multivariate Granger causality to th

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XML im Kontext der Systemarchitektur that human decision making does not follow this theory. Specific patterns of activity in several brain circuits identified in recent years have been associated with irrational and imperfect decision making. Brain activity modulated by dopamine and serotonin is assumed to be among the main drivers o

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,Einfügen, ?ndern, L?schen von Daten,lity” caused by the difference of preferences declared and preferences followed; (iii) the choice of proximity measures in knowledge and preferences fusion; (iv) ways to a systematic design of approximate DM; and (v) the control of the deliberation effort spent on a DM task via sequential DM. The ex

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https://doi.org/10.1007/978-3-8348-2185-0tends to select too many causal relationships, which leads to spurious results. In our previous work, we proposed a thresholding strategy for Graphical Lasso Granger method, called two-level-thresholding and demonstrated how the variable over-selection of the Graphical Lasso Granger method can be ov

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Datenbankprogrammierung mit PHP,process to rationally constrict the pertinent features towards a manageable selection of diagnostic findings. Without the suitable incorporation of domain expert knowledge machine based selections are often polluted by noise or irrelevant but massive variations. Selecting a subset of features is nec

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