Probability design and inverse probability design are also researched which lay a solid foundation for designing safe and reasonable blisk.
A stratified multistage clustered probability design was used to select a population-based sample.
A novel experimental design method, referred to as Gaussian Probability Design, is proposed and compared with the conventional Factorial Design.
A two-stage stratified probability design produced a representative sample of 1,250 preschoolers aged 3 to 5 years.
Within the RGD framework, each candidate design in the design space is analyzed for its safety state (in terms of failure probability), design robustness, and cost.
The probability design allowed extrapolation to the sampled population of 17,059 km in urban rivers and a nearshore area of 11,091 km2 in the Great Lakes.
The Gaussian probability design is based on an assessment of the probability that the experimental data near a class boundary belongs to a specific class.
Two new experimental design methods, referred to as the Gaussian probability design and the fuzzy boundary design, are proposed and compared with a conventional factorial design.
A population-based sample (N = 229) aged 50 to 68 years was selected with a multistage probability design from a multiethnic population living in Cook County, Illinois, United States of America.
Both the Gaussian probability design and the fuzzy boundary design methods automatically search for regions, where training data are sparse, and add pairs of training data on both sides of a class boundary where the data density is the lowest.
The Gaussian Probability Design automatically searches for the sparseness of the data, and adds pairs of training data on both sides of a class boundary in areas where the data density is the lowest.
