Non-random ascertainment is commonly used in genetics research to maximize the amount of information in the data for a given sample size (e.g., Elston and Sobel 1979).
Fig. 5 Contour plot of the posterior distribution (4) in the plane for a given sample of 18 Δ E values corresponding to a scenario 1, b scenario 2, c scenario 3, and d scenario 4. Additionally, dashed and dotted lines confining the region D P∗ are shown for three different values P ∗=0.8, 0.85, and 0.9 together with corresponding probabilities P calculated according to Eq. (7).
For a given sample (i.e. a pH/aw data pair), it could be easily assessed if the product would likely be "safe" for 9 days at temperatures of <2 °C, 2 4 °C etc. by simply plotting the data point in the chart.
The R values, i.e. the ratio of number of anions to cations (O2−/(Zn, Pd)2+) for a given sample, are also tabulated.
For a given sample, three independent determination were carried out.
For samples y1,..,yh, simulate grades g1,..,gh from the observed empirical distributions of grades given to the sample (i.e. for a given sample, with probability of selecting grade j proportional to the proportion of raters who assigned grade j to the sample).
Successful genotyping for a given sample was defined proportionally as <10% missing data (i.e., fewer than 19 of 191 SNP genotypes per individual).
Consider the scenario often encountered when quantifying biological systems where resources are available for only four replicates of a given experiment, i.e. only four data points are generated for a given sample time t i.
