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Recently, Wang et al. (2012) proposed threshold models using the GS framework and extended three Bayesian methods (BayesA, BayesB, and BayesC π ) to estimate genomic breeding values of animal threshold traits with more than two categories; they named these methods extended BayesTA, extended BayesTB, and extended BayesTC π.
This paper also shows that the direct feed-through term in the AW compensator does not contribute to enlarging the achievable domain of attraction within the multiplier GS framework.
Gianola et al. (2006) proposed nonparametric and semi-parametric methods to model the relationship between the phenotype and the markers that are available within the GS framework.
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The R / O / G framework is highly recommended.
The CPPC-G framework is a service-based architecture designed to provide efficient fault-tolerant mechanisms for the execution of sequential and parallel applications on grids.
During previous applications of Schoenfeld's R / O / G framework to detailed analysis of teacher decision-making (Schoenfeld 2008), the researcher had inferred the teacher R / O / G from classroom video.
We highlight the need for a landscape community genomics (LCG) framework to help to motivate and challenge scientists in diverse fields to consider a more holistic, interdisciplinary perspective on the genomic evolution of multi-species communities in complex environments.
The superreplication problem is namely solved in a more satisfactory way in the G-framework.
In this section, we introduce some notations and preliminary results in G-framework which are needed in the sequence.
In the G-framework, stochastic functional differential equations were introduced by Ren, Bi and Sakthivel [20] and then generalized by Faizullah [21, 22].
The study of mild solutions for stochastic evolution equations in the G-framework was given by Gu, Ren and Sakthivel [18].
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