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The prediction model for preference of electronic questionnaires in TKR patients had an r of 0.41 with an optimism estimate of -0.24, indicating absence of optimism.
The prediction model for preference of electronic questionnaires in THR patients had an r of 0.31 with an optimism estimate of 0.04, yielding an optimism-corrected r estimate of 0.27.
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It will then come as no surprise that the vocabulary of noncommutative harmonic analysis is well suited to describing the invariant Gaussian field model for orientation preference maps in V1, since the key features of this model rest on the action of (mathit{SE}(2) ) on the function space of orientation maps.
Thus, we analyze the procedure to determine weights, and proposed two extended models for preference voting and aggregation.
In the next section, we develop the models for preference aggregation to assess the weights associated with different ranking places.
Using fuzzy relational modeling for preference visualization, the FURIA fuzzy decision support system aims to facilitate preference alignment and group agreement.
Three simple models for preference ranking order fitted the data well.
Participants viewed and evaluated the digital landscape model views for preference (n = 77) or estimated the presence of complexity (n = 34) or coherence (n = 38).
This leads to the following state space model for online preference data: (19).
We therefore study a Bayesian feature selection scheme that can learn a sparse and well-generalizing model for observed preference data.
Since the preferred steering vector is unknown and we want to predict future values for the tuning parameter, we introduce stochastic variables and and propose the following probabilistic generative model for the preference data: (8).
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