Uncertainties may originate from model structure, estimation of model parameters, data input, representation of natural variation and scaling exercises.
To estimate the model parameters, data from the metamorphosis assays were converted to percentage metamorphosis success and analyses were conducted using the R statistical platform [57].
Exclusion of inconsistent respondents made no notable impact on the findings (in terms of significance and rank of the model parameters; data not presented).
Consequently, the prior effectively acts as a guide for the analysis, with the final estimates representing the result of an optimal combination of substitution model parameters, data, phylogeny and priors (this is a strength of all Bayesian methods).
In particular, the proposed methodology allows deriving the model structure and estimating model parameters using data by means of identification techniques.
However, estimating model parameters from data with limited diffusion gradient strength, such as clinical scanners, has proven unreliable due to a shallow optimization landscape.
All other model parameters and data for human activity modules are identical to those used by Pokhrel et al. (2015).
We also propose a kernel-based least-mean-square algorithm for updating the model parameters using data collected by each sensor.
In this work we present the techniques for efficient estimation of model parameters from data.
In order to yield a predictive model we determined the value of all five model parameters from data.
We estimated relevant model parameters from data recently collected from Hunan and Jiangxi Provinces [24], so that the predictions would apply to the transmission dynamics of the lake and marshland areas.
