The potential activation of multiple biological pathways and system-wide responses in vivo creates a substantial challenge to the understanding of stochastic mixtures containing a distribution of surface hydrophobicity.
The results for all three kernels on a stochastic stimulus mixture are the same as the main analysis above using the bursting kernel and sinusoidal stimuli: we obtain accurate estimates of all parameters only if we apply the correct model to the corresponding data.
Stretch-induced changes in stretch in stress fibers were estimated using a stochastic constrained mixture model of stress fiber networks described previously [7].
We also tried other pairs of stochastic stimulus mixtures (results not shown) and found that the more similar the two stimuli are, the more the rejection ratios tend to decrease, whether using the correct or the incorrect model, and if two stimuli are more different, all rejection ratios tend to increase, including rejections of the true model.
We call D s M the space of stochastic phylogenetic mixtures.
This paper investigates the issue of reliability assessment for engineering structures involving mixture of stochastic and non-stochastic uncertain parameters through the Finite Element Method (FEM).
In this paper, a design approach based on the combination of robust stochastic adaptive method and mixture density network (MDN) is proposed for general nonlinear ARMAX models with uncertain parameters and with non-Gaussian random inputs.
Using stochastic collocation, the subfilter mixture fraction dissipation rate model form error as well as the uncertainty in a model parameter are propagated through LES calculations of the Sandia D Flame utilizing the steady flamelet model.
These ideas are correct if two unspoken assumptions are fulfilled: first, that the diversity of chemicals which make up human exposures act in strictly independent ways, according to the stochastic principles of the mixture concept of independent action [ 8].
