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Finally, our model was developed using multiple assumptions and extrapolations.
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flexible exhaustive solutions: arbitrary number of (potentially overlapping) biclusters with additive, multiplicative and symmetric assumptions using multiple ranges of values; biclustering behavior dynamically adapted to deal with varying levels of noise and missing values.
We imputed missing values using multiple regression based on the assumption that missing values are random.
Missing data will be imputed using multiple imputation methods if the assumption that missingness is at random seems justifiable.
Missing values will be imputed using multiple imputation when meeting the assumption of missing (completely) at random, as imputation will increase precision and possibly reduce bias.
This paper extends these solutions through a new generalized calculation method for recovery systems using multiple wells, drains, etc. Simplified assumptions allow the introduction and use of potential functions for a mathematical representation of the coupled stratified immiscible fluid flow that satisfy partial differential equations with coupling conditions on the interface.
Data heterogeneity due to using multiple populations may conflict with model assumptions used in linear regression methods.
Phylogenetic analysis using multiple sequence alignments rests on the assumption that the residues (nucleotides or amino acids) at the same position in every sequence in the alignment are homologous.
This is important to understand since the assumption is that by using multiple wavelengths this noise source may be able to be canceled out.
Using multiple interpretations of a system by making different assumptions can thus be used to understand processes in data limited groundwater systems.
Missing values will be replaced using multiple imputation methods based on observed values with varying assumptions.
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