It has been retrieved by a Bayesian data inversion [11] of a set of 300 numerical simulations on a Somma-Vesuvius and surrounding plains Digital Elevation Model, produced by sampling a wide matrix of input parameters typical of Vesuvius PDCs [12].
The results of these 8135 simulations, represented as a 3-way array of 8135 observations x 16 state variables (corresponding to the 16 coupled differential equations in the dynamic model) x 200 time points in each trajectory, were related to the matrix of input parameter combinations (8135 x 9) in the metamodelling study described below.
In order to visualize the relative influence and importance of each input parameter, surface and contour plots have been produced in MATLAB by providing the ANFIS developed model with matrices of the input parameters.
The primary purpose of using pre-processing technique was to distinguish the suitable and appropriate constituents of the input parameters' matrix, to eliminate redundancy, to enhance prediction power and calculation efficiency.
The QDE approach employs three spatially related matrices: (1) covariance of input parameters, (2) sensitivity of model outputs, and (3) covariance of model outputs to identify the most important location to explore based on a specific design.
Three input parameters, the matrix data loader, the condition pool, and the gene mapping, are fed into the SAGE data filter, the core of the data processing unit.
The first file (all job data) contains a dump of the input parameters, generated dissimilarity matrix, and coverage data on taxa.
Dense matrix multiplication is a core component of many high-performance computing and machine learning algorithms, but the performance of matrix multiplication algorithms can vary significantly based on input parameters and hardware architecture.
A sensitivity analysis was undertaken to evaluate the dependence of model predictions (percentages of RK and multiresistant strains in the colonized population) on small variations of all input parameters (such as the contact matrix, antibiotic exposure levels, etc).
The input parameters are a gene expression matrix, E, the correlation threshold value, θ, the minimum number of rows, mnr, and the minimum number of columns, mnc.
Both approaches take as input parameters: 1) a binary adjacency matrix which indicates which pairs of genes are correlated above a certain threshold, 2) the number of genes the user wishes to directly measure, and 3) the fold redundancy.
