The method presented here for the determination of the infection parameters differs from previous mathematical modeling approaches to viral dynamics in that we have considered the explicit dependence of two experimental quantities on the parameters, rather than fitting a full dynamical model to the time-course of an experiment.
Bayes' theorem then takes the form Once we specified the prior distributions for μ, ∑, and σ, the next step was to calculate the posterior distribution conditioned on the observed data and to calculate the statistical quantities for the parameters of interest.
The turbulent quantities of the parameters such as the mean temperature, turbulent heat flux, and temperature variance were obtained by direct numerical simulation (DNS).
For estimating an optimized quantity of target, the parameter-dependent approach requires some common and individual parameters.
To estimate an optimized quantity of the target, the parameter-dependent approach is simple because it only requires the individual chromatographic parameters of linear response and the individual recovery ratios of all ligands of interest from a PMFS, besides some common parameters (additional file 1).
Because the number of documented cases is small in comparison to the quantity of possible parameter combinations, the algorithm was designed to avoid overlooking of similar cases (high sensitivity, low specificity).
Table 3 The matching time and quantity of Sigman Parameter Sigman The initial value Values 0.01 0.16 0.32 0.5 1 2 4 Matching quantity 45454546454545 45 45 Matching time 0.011 0.011 0.011 0.011 0.011 0.011 0.011 Fig. 10 The tendency chart of Sigman matching time and quantity.
The physical quantities of the key parameters that define our model space are summarized in Table 1.
Table 4 The matching time and quantity of thresh Parameter thresh Initial value Values 0.01 0.02 0.03 0.04 0.1 0.3 0.5 Matching quantity 47 47 47 46 28 5 0 Matching time 0.02 0.014 0.012 0.011 0.005 0 0.001 Fig. 11 The tendency chart of thresh matching time and quantity.
Table 5 The matching time and quantity of r Parameter r Initial value Values 0.01 0.5 5 10 20 50 100 Matching quantity 59 7 28 46 56 62 62 Matching time 0.017 0.01 0.011 0.016 0.018 0.0180.018 Fig. 12 The tendency chart of r matching time and quantity.
Table 8 The matching time and quantity of magnif Parameter Magnif Initial value Values 0.01 0.5 2 3 4 12 18 Matching quantity 0435146271427 14 1 Matching time 0.017 0.018 0.014 0.011 0.006 0.002 0.003 Fig. 15 The tendency chart of magnif matching time and quantity.
