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In a probabilistic framework, conditional clade probabilities (CCPs, cf. Fig. 1) provide an accurate approximation of posterior probabilities for a very large number of tree topologies from a smaller MCMC sample (Höhna and Drummond, 2012; Larget, 2013; Szöllősi et al., 2013b).
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Instead, matching needs to be taken into account in the framework of conditional logistic regression with maximization of conditional likelihood.
In developing the framework, the conditional probabilities of different flood events under various extreme precipitation scenarios are estimated by exploiting the copula-based model.
In the framework of conditional knowledge bases and default extensions, it implies that any rational inference relation may be considered as the rational closure of a minimal knowledge base.
The estimation to test these predictions is based on the standard empirical neoclassical growth framework for conditional convergence.
Adjustments for parameters whose distribution among cases and controls differed significantly were made within the framework of conditional logistic regression.
Using the sequential framework of Conditional Random Fields (CRFs), sentences in abstracts are labeled with the four rhetorical roles of Aim, Method, Results and Conclusion.
In the near future, it is possible to utilize these marks better by introducing different model frameworks, such as conditional random field (CRF [ 28]) framework, which can estimate the weight for each feature automatically and predict the binding loci more precisely.
In addition, results also confirm that the developed framework can estimate conditional probabilities associated with different flood events under various extreme precipitation scenarios aiming for flood control and sediment transport.
Thus, the distinction is represented in the precise probabilistic framework in the conditional probabilities.
Therefore, we believe it is a promising framework for future conditional analyses in the MHC.
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