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The independence model, homogeneous agreement model, and non-homogeneous agreement model form a nested sequence of models.
The transition probability of the Markov chain governed the sequence of models in the chain, which were a subset of the finite number of possible models.
A sequence of models (or states) were developed that adhered to the fundamental Markov chain rule (i.e., the probability of the next model depends only on the current model).
Structural equation modeling (SEM) was used to test a sequence of models that examined the mediating effects of opportunity recognition and innovativeness on the relationships (paths) between risk-taking propensity, entrepreneurial intensity, and firm sustainability (Joreskog and Sorbom 1993).
(For a review of these mechanisms see [17].) It is one of a long sequence of models developed from 1998 to the present, with notable contributions by Markram, [18], and Dittman, Kreitzer and Regehr [19].
The whole parameter sequence is then calculated from that complete sequence of models, taking into account the dynamics of the synthesized parameters thanks to the first and second derivatives of the parameters.
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On the basis of the sequence of model selection, two hybrid models (ARIMA-MLP and MLP-ARIMA) are presented in the next section.
So until now, to the best of our knowledge, there is very little research on the limit behavior of the iterative sequence of model (2.1).
We used the genome sequence of model legumes, together with their known synteny to the pea genome to identify candidate genes for the A and A2 loci in pea.
The regularized models produce a sequence of model fits corresponding to different values of the regularization parameter lambda.
For this purpose the genome sequence of model organisms (see Additional file 9) were also searched by using tBLASTN [ 75].
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