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In many hybrid simulations, including those that have used UI-SimCor, analytical models with few effective degrees of freedom are typically used.
Simple models with few parameters seldom can describe the real complexity of the phenomena arising during the heating of the various substances.
However, existing mathematical models with few state variables and fixed parameters are inadequate in characterizing the temporal transformation of the landscape.
The analysis is not restricted to minimal models with few non-linear terms, in fact, the novel approach adopted here is to consider differential equations containing highly non-linear terms in both the damping and restoring coefficients.
However, these works often assume speaker-dependent models with few target speakers that are known during training.
The development of initial prediction models with few independent variables (one to two at most), also helps to obtain more trustful models when few data is available.
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Throw-away cameras, auto-focus and digital models with fewer movable (hence breakable) parts, took a toll on the business.
By using log-likelihood ratio tests, this analysis determined if the fully parameterized IMa model explains the data significantly better than a series of simpler models with fewer parameters.
Models with fewer than 250 new and 250 used cars sold were excluded from the analysis.
Models with fewer parameters are therefore preferred on the grounds of parsimony.
Objective functions such as this quickly force models into a reduced feature space by favouring models with fewer descriptors.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com