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These advantages include, but are not limited to, the ability to incorporate time-varying predictors, handle dependence among repeated observations in a very flexible manner, and to provide accurate estimates with missing data under fairly unrestrictive missing data assumptions.
How to weight evidence from different datasets and handle dependence between them, efficiently estimate and critically assess complex models are key challenges that we expound in this paper, using examples from influenza modelling.
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Instead of using complex behavior graph, we propose the Handle dependences and Probabilistic Ordering Dependence (HPOD) technology to represent the program behaviors.
A second is to build a joint model that needs tackling with new computational methods (Tom et al., 2010) (see Section "Handling dependence between datasets").
It provides less conservative control than the traditional Bonferroni, Šidák, and Holm corrections and is able to handle the dependence (e.g. co-regulation) among gene expression profiles.
To handle this dependence, we first choose an arbitrary internal node in τ as the root; this imposes an orientation on each branch.
Last, the development of hierarchical occupancy models, with their ability to handle spatial dependence and imperfect detection, paves the way for better modelling of the underlying sources of uncertainty [ 8].
We had disapproved of how our own mothers had handled their dependence on men.
In particular, it has shown that the model needs further development to rigorously handle magnitude-dependence in the proportion of aftershocks and in the b-value.
However, the number of dependencies that can be represented is limited, and this algorithm cannot be extended to handle high-dependence relationships.
Even though loglinear models have been proposed by multiple authors for handling conditional dependence in HIE, implementation of such models requires customized programs and the process for choosing pairwise interactions in these models has not been specified.
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