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Semantic models for intensity of belief and use of gamification as a strategy allow novel aspects of influence to be taken into account as well.
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Table 3 summarizes the results of the regression models for intensities VII X using Eq. 3.
Only the damage-related scenarios given in Guettiche et al. (2017) were considered for testing the loss models for intensities VII X.
It is true that globally, the set of local simple models represent a complex model for intensity distribution modeling.
The emission model for intensity data are a bivariate Gaussian distribution.
We implemented two different emission models, a discrete model for genotype call data, and a continuous model for intensity data.
Our emission model for intensities is heavily parameterized as a bivariate normal mixture model over 36 diplotype states at each marker locus.
We tested separate models for the intensity and size tasks.
The figure illustrates that the values of these covariates may change with the advance of time (e.g. new tweets, new follower relationships, etc).. Specifically, our models for the intensity functions λ + ( i, t | β +, H t − ) and λ − ( i, t | β −, H t − ) are Cox proportional hazards models [15], taking the form λ + ( i, t | β +, H t − ) = λ 0 + ( t ) exp [ β + ⋅ s ( i, H t − ) ] (1).
Additionally, multiple regression models for integration intensity were applied, as described in [55].
Models for traffic intensity and traffic load were additionally adjusted for background NO2 concentrations.
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