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It is reasonable to assume that the first occurrences of an outcome among different individuals are independent, which is an important assumption in modelling these events.
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To model these events within our spectral estimation, a noise spectrum is included in every tone object.
An important stage in this process is the identification of the dominant micromechanical events and the development of constitutive relationships which accurately model these events.
A hierarchical linear modeling approach, using the Poisson model with equal exposure and a logarithmic link function, was used to model these events.
Some methods take as input gene trees allowing duplications and losses (Lajoie et al., 2010; Muffato et al., 2010) but do not model these events and treat them as noise that is removed for the construction of chromosomes by traveling salesman-like optimization methods.
For the control task, the data were analyzed by grouping each trial into 1 of 4 event types based on distractor location (left of right) and congruency and modeling these event types with a GLM as previously described.
Because the number of 'know' responses was small, these were categorized as CHs, since modelling these as events of no interest would have resulted in too few CHs in about half of our subjects.
This was our motivation for resorting to extreme-value theory, which provides a framework and tools to model these extreme events.
This paper presents the methodology followed to model these transient events with the TINTE code, as well as total power, maximum and average fuel temperatures and case comparison results.
The difficulty in modeling these rare events is inherently rooted in the overwhelming number of degrees of freedom described explicitly in such statistical simulations, which is conducive of entrapment in local free-energy minima.
An emerging approach is to leverage graph-based modeling of these events, to understand ongoing trends and predict future ones.
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CEO of Professional Science Editing for Scientists @ prosciediting.com