The main idea of our analysis is that the errors can be described by a mixture of Gaussian distributions rather than a single Gaussian distribution.
The increasing of chloride diffusion coefficient can be described by a linear function of frost cycles.
Water molecule diffusion can be described by the diffusion tensor and diffusion coefficient D; as D increases, the diffusion speed increases.
The directional dependence of water diffusion in tissue can be described by a three-dimensional (3D) diffusion tensor for each voxel.
The surface diffusion process can be described by the linear response theory in which the diffusion coefficient D is a function of system temperature and friction coefficient η.
The distribution of transferrin intensity, in the majority of endosomes, can be described by a Gaussian distribution (dark grey line).
Hence diffusion can be described using a relatively simple one-dimensional model with the axis of diffusion perpendicular to the boundaries between tissue, ACSF and agarose.
This process is often regarded as Brownian motion, which is sometimes called Gaussian diffusion or normal diffusion, and this process can be described by employing the Laplacian operator.
The observed broadening of the nuclear peak can be described using the Gaussian distribution function.
Under the assumption of normal distribution, each eigenvalue can be described by its expectation and variance.
Gaussian Random Process Regression is based on the general assumption that measurements at points that are close to each other in the parameter-space co-vary in a way that can be described by some covariance function.
