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Numerical implementation of constitutive laws involves specific incremental methods.
It is accurate compared to traditional incremental methods, which are prone to error propagation.
The main goal of the research work is to analyse the different incremental methods and to optimize their efficiency.
In contrast to existing incremental methods, the complexity of our approach does not depend on the number of users and items.
We introduce incremental methods for estimating the conditional probability and present principles for updating probabilistic approximations when adding and removing objects, respectively.
In contrast, incremental methods do not require the number of intervals, but they require the stopping criterion to stop the discretization process in order to yield the best number of intervals of each attribute.
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For the incremental method, the step between validations is incremented by a factor of 5 elements each time and we observe that the response time is dependent on the size of the step.
The number of motor units (MU) was estimated by the incremental method (the number of different single twitch tensions produced by stepwise increments of stimulus intensity).
Iteration of this method leads to a powerful incremental method.
First of all, it should be mentioned that the incremental method as basis of the extended incremental method is just one of various methods to linearize nonlinear functions.
Wesley et al. [11] proposed an incremental method for three holistic windowed aggregates.
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