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This work presents a formulation of reduced SEA models for incomplete systems defined by a set of effective loss factors.
Some results suggest the assumption that protection approaches 100%% at high assay values may not hold; models for incomplete protection are considered.
We use a single chain and non-informative prior for all imputations, and expectation-maximization (EM) algorithm to find maximum likelihood estimates in parametric models for incomplete data and derive parameter estimates from a posterior mode.
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Our chosen model for incomplete cage formation was the NAUYU QWLKD GGPSS GRP AA sequence.
Incorporating sampling frequencies into the models controlled for incomplete sampling of our phylogenies.
Separate logistic regression models for each incomplete covariate were used to model the probability of the covariate being missing according to the appropriate missing data mechanism (Tables 2 and 3).
Data were analyzed in a multi-scale occupancy model accounting for incomplete detection of collisions.
The model allows for incomplete information about the world, the occurrence of exogenous (unplanned) events, unreliable sensors, and the possibility of an imperfect causal theory.
The comparison between two-dimensional and three-dimensional models evidences that three-dimensional models are needed for incomplete wetting conditions.
R osetta's knowledge-based energy function and large-scale sampling strategies allow for construction of models from incomplete or limited experimental data sets.
In this paper we establish an error model for the incomplete "mark" and imperfect "destroy" operations.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com