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We focus our discussion by simplifying the model setting and carrying out 15 simulations.
In Sect. 2 we define the mathematical model setting and introduce the concept of level crossings and specifically the upward crossings.
The primary method of investigation is a controlled experiment in which a model specified by Dodds, Monroe and Grewal [J Mark Res 28 (1991) 307] provides the theoretical model setting and in which measurement procedures are experimentally manipulated.
Again, this idea follows the reasoning of the multi-model inference approach, which advocates that uncertainty should not be ignored, either for parameter estimation or for model setting and selection [ 7].
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We use a novel MBMD measure to decide the interpolation model sets and weights.
A wrongly recognized context will lead to choosing the wrong model set and event priors.
Let the number of pool speakers be L. Let ϕ1,…,ϕ L denote the neutral model sets, and ψ1,…,ψ L denote the emotional model sets.
A dominant model plus uncertain terms is derived from these model set and used as an observer.
By being speaker-dependent, we mean that the interpolating model sets and weights are dependent on the speaker identity.
In our approach, we need a distance measure to find the closest model set and the interpolation weight.
In our system, if the closest neutral model set and the closest emotional model set are from the same pool speaker, the system simply uses the second closest emotional model set.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com