Bayesian methodology is usually underpinned by the use of the Markov Chain Monte Carlo (MCMC) method especially the use of the Gibbs sampler (see Gelfand and Smith 1990; Casella and George 1992 for exposition on the Gibbs sampler proposed by Tanner and Wong 1987) to augment data.
The Bayesian sample size proposed by (Whitehead et al., 2008 [27]) for exploratory studies on efficacy justifies the acceptable minimum sample size by a "conclusiveness" condition.
Another shortcoming was the failure to recruit the sample size proposed by the power calculation 27.
The cut-off limit during wine intervention was set at EtG <0.5 mg/ml (2.2 μmol/l) (negative sample), as proposed by Bottcher et al. [ 24].
The qRT-PCR-based miRNA-expression data in this study were normalized relative to the mean expression value of all miRNAs per sample, as proposed by Mestdagh et al. [ 35].
We used the LHS sampling scheme, a type of stratified Monte Carlo sampling first proposed by Mc Kay, Conover and Beckman [25] and later applied to deterministic mathematical models, in particular by Blower et al. [26].
Indeed, only two alternative samples were proposed by MSTRAT, with only one individual differing between the two samples: Rich baba rose faux versus Kizil.
The sampling protocol proposed by Loveland et al. [8] was introduced to identify spatial variation of land use/land cover change in the conterminous United States using Omernik's 84 Level III Ecoregions [9] as the sampling framework.
In this study, the sensitivities for different sampling designs proposed by the Spanish authorities for the commercial poultry population of Catalonia were assessed, using the scenario tree model methodology.
To be specific, the R function utilizes a Metropolis-Hastings within Gibbs sampling algorithm proposed by Cowles [ 30].
