Bayesian approaches with random-effects and fixed-effects models were used to synthesise data.
The strategy is also adopted in our approach with random projection theory [11].
This method is based on the wrapper approach with random search strategy for subset channel selection.
Two approaches have been used to model the randomness of the column capacity: (1) calibrated Cubic Rankine Gordon curve (CRG) approach with random representation of σc and E∗ and (2) column capacity distribution fitting.
Besides providing spatial predictions of soil water retention at the catchment scale, the study presents a detailed methodological insight to model setup and validation of the underlying machine learning approach with random forest.
We also study the relation of the minimum volume set approach with random set theory when α is a random variable and extend the most specific probability possibility transformation proposed in [System Theory, Knowledge Engineering and Problem Solving, in: Fuzzy Logic: State of the Art, vol. 12, Kluwer, 1993, pp. 103 112] to continuous piece-wise strictly monotone probability density functions.
This paper develops a RBD approach that integrates a Monte Carlo Simulation (MCS -based RBD approach, naMCS -basedxpanded RBD approach, with ranamelyield theory to modexpanded diRBDtly approachicitly, the inherent spatial variability of soil properties in RBD of drilled shafts.
In order to account for the multilevel structure of the data (children nested within households within clusters within states) and to account for clustering, we used a multilevel modeling approach, with random effects specified for households, clusters, and states.
In order to account for the multilevel structure of the data (children nested within households within clusters nested in states) and to account for clustering, we used a logistic multilevel modeling approach, with random effects specified for households, clusters and states.
In the regression analysis a multi-level approach with random intercept was used.
Therefore, combining the rational approach with random approaches (e.g. transposon mutagenesis) may result in the identification of novel genes involved in efficient biodiesel precursor production.
