Sentence examples for model two random from inspiring English sources

(RP_1): For each of the three inputs of the Quadratic model two random values were selected in the range of ([-100, 100]) (valid inputs), and two were selected in the range of ([-1000 [-1000cup ]100, 1000]) (invalid inputs).

The test sets generated for this model have the following parameters: (RP_1): For each of the three inputs of the Tiny model two random values were selected in the range of ([-100, 100]) (valid inputs), and two were selected in the range of ([-1000 [-1000cup ]100, 1000]) (invalid inputs).

The test sets generated for this model have the following parameters: (RP_1): For the temperature input of the Flow Control model two random values were selected in the range of ([-100, 300]) (valid inputs), and two were selected in the range of ([-1000 [-1000cup ]300, 1000]) (invalid inputs).

The test sets generated for this model have the following parameters: (RP_1): For each of the eight inputs of the Duplex model two random values were selected in the range of ([-1000 [-1000) (valid inputs) and no values were selected outside of the domain input because no further improvement could be achieved.

The test sets generated for this model have the following parameters: (RP_1): For each of the three inputs of the Quadratic model two random values were selected in the range of ([-100, 100]) (valid inputs), and two were selected in the range of ([-1000 [-1000cup ]100, 1000]) (invalid inputs).

The number of test data created is the same as set (RP_1); (Random Random generation as the previous one but the number of test data was taken from the cardinality of (RP_2); (RP_1): For each of the three inputs of the Tiny model two random values were selected in the range of ([-100, 100]) (valid inputs), and two were selected in the range of ([-1000 [-1000cup ]100, 1000]) (invalid inputs).

As proposed by Burgueño et al. (2007), pedigree-derived additive and additive × additive relationship information can be combined into a single model that will model GE using the FA model with two random effects: one is a regression on pedigree additive relationships, and the other, a regression on pedigree epistasis additive × additive relationships (see Appendix).

For the methodology part, the Poisson model and two random effect models with a Bayesian inference method were employed and compared in this study.

Liu et al [ 19] proposed a multi-level two-part random effects logit-lognormal model; two nested random effects were included in each part to model the nested clustering structure in a data, assuming the respective random effects in the two parts followed a bivariate normal distribution.

Thus, (3A) and (4A) become and Similar to Burgueño et al. (2007), pedigree-derived additive and additive × additive relationship information can be combined into a single model by extending (5A) to a model with two random effects, one of which, g P ∼ N 0, G0P⊗ AP), is a regression on pedigree additive relationships.

Thus, the genomic-derived additive and additive × additive relationships can be combined into a single model by extending (5A) to a model with two random effects, one with gM∼ N 0, G0M⊗ AM), representing a regression on genomic additive relationships, and the other representing a regression on genomic epistasis additive × additive relationships,.