The phrase "allow uncertainty" is correct and usable in written English.
It can be used in contexts where you want to express the idea of permitting or accepting a lack of certainty or clarity in a situation.
Example: "In scientific research, it is important to allow uncertainty in your findings to account for variables that may not have been considered."
Alternatives: "embrace ambiguity" or "accept doubt".
Together with the president of the European Central Bank, Jean-Claude Trichet, the officials agreed Wednesday that they could no longer allow uncertainty about the future of Greece — and the euro zone — to disturb global investors.
"We cannot allow uncertainty for parents or pupils to persist".
Preference relation, utility function, and outranking are not strictly based on objective importance in values, which allow uncertainty to a certain degree.
The powerful semantics are the sort of semantics that allow uncertainty (that is, the representation of degree of membership and degree of certainty) and, therefore, allowing abductive or inductive reasoning.
Clarifying whether a script is a potential script, planned script, or performed script would allow uncertainty to be better considered, and would ensure (where possible) that new designs of control measures are based on probable scripts developed using empirical data, rather than anecdotal or purely hypothetical scripts. .
The direct quantification of discrimination factors may be especially important with the increasing use of Bayesian mixing models that allow uncertainty in discrimination factors to propagate through the analysis, returning a true probability distribution of estimated dietary proportions, e.g. [21], [22].
Specifically, we allow uncertainties for both the interference and target impulse responses.
If he has largely made that work until now by avoiding risky decisions, its inherent flaw may just have been revealed as the collapse of the peace process allows uncertainty to rush in.
This is an evening of admirable clarity and the storytelling never falters, although sometimes the acting does, particularly in the second half (because of under-rehearsal?), allowing uncertainty to creep in.
Additionally, Bayesian data analysis, which provides a probabilistic framework for data analysis, is considered in detail, since it allows uncertainty quantification and validation of the model selection step.
The problem addressed is the design of linear filters having an error variance with an optimized guaranteed upper bound for any allowed uncertainty.
