In addition, the 'mixed' model achieved uncertainty estimates similar to the 'single' model while increasing the number of available reaction wells per instrument run.
However, while key improvements were achieved, uncertainty about the future of telehealth remained, and the appropriateness of employing these methods to embed complex innovations into routine practice is less certain, particularly when, for interventions like telehealth, consensus around evidence of clinical and cost effectiveness is yet to be established.
We propose simple methods to achieve uncertainty propagation, parameter optimisation and sensitivity analysis in cases where the model satisfies some monotonic properties.
Multiple hypothesis testing is then conducted to construct an ensemble of reduced-dimensionality PCEs with only the most influential terms, which is meaningful for achieving uncertainty reduction and further acceleration of parameter inference.
It is established that controllers which are synthesized on the basis of a reduced-order slow model, and achieve uncertainty decoupling or uncertainty attenuation, continue to enforce these objectives in the presence of unmodeled dynamics, provided that they are stable and sufficiently fast.
Whereas modified imputation was clearly superior for RASTA features, better results for MFCC features have almost consistently been achieved by uncertainty decoding, even though uncertainties were estimated in the spectrum domain for both features and propagated to the recognition domain of interest.
Experiments over a period of years in Japan with a superconducting, 30g mass supported by diamagnetic levitation never achieved an uncertainty better than ten parts per million.
Much work towards that end is ongoing, though no alternative has yet achieved the uncertainty of 20 parts per billion (~20µg) required to improve upon the IPK.
The memo claimed that "victory" would be achieved when "uncertainties" (read: doubt) became part of the conventional wisdom among the public.
When completeness or accuracy of estimates cannot be achieved, high uncertainties in input data can be overcome by applying the conservativeness principle [2], which guarantees that the reduction of emissions is not overestimated, or at least the risk of overestimation is minimized.
When completeness or accuracy of estimates cannot be achieved, high uncertainties in input data can be overcome by applying the conservativeness principle [ 2], which guarantees that the reduction of emissions is not overestimated, or at least the risk of overestimation is minimized.
