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Non-parametric models like the artificial neural network (ANN) based models can predict software reliability based on only fault history data without any assumptions.
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Agent-based models can predict system-level properties of populations from stochastic simulation of fine-scale movements.
However, spread models for species that already exist in Europe have shown that grid-based models can predict spread rates similar to the realized rate (Pukkala et al. [2014]).
RSM-based models can predict the relationship between a limited number of input and output parameters and hence cannot be applied for highly nonlinear processes [ 18].
For example, a vibration model of a rotating system based upon the existing bearing models can predict only the purely in-plane type motion on the flexible casing plate given only the bending motion on the shaft.
Maximum entropy models can predict species distribution based on the geographic characteristics from the location of modern specimens29,30.
Our models can predict aGVHD and non-GVHD based on MS spectral data while avoiding invasive tissue biopsies.
Based on our assessments with the real criminal incident data, both models can predict future incidents accurately.
Econophysics models can predict economic stall.
Statistical models can predict when failure may occur.
Numerical simulations based on the model can predict the grain density of magnesium alloys containing SiC particles, using the diameter and mass fraction of the particles as inputs.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com