Sentences with phrase «observational error»
Observational error refers to mistakes or inaccuracies that occur when someone is observing or gathering information. It means that the person might make a wrong or flawed observation, leading to incorrect or unreliable data. Full definition
In observational error, you've omitted the biggest one: the sample isn't a representation of the population.
realclimate.org
This approach allows us to neglect observational errors and model biases, and is therefore referred to as the perfect modeling framework.
Observational errors on any one annual mean temperature anomaly estimate are around 0.1 deg C, and the errors from the linear fits are given in the text.
As an extension, systematic observational errors could perhaps be corrected as part of the regression by estimating a constant shift to apply to each thermometer (treating changes in technology as creating a new thermometer on the same site), though this may make the problem too large.
Each day, automated and semi-automated quality control systems identify observational errors using methods such as comparison with data from nearby sites.
The final list of members should contain only a very few nonmembers — either those that appear to agree with the group motion because of observational errors or those that happen to share the group's motion at the present time but are not related to the group historically.
This approach assumes, as I think we must, that thermometers differ from each other by a constant shift (and random observational errors).
Although apparent irregularities do occur, these are due to observational error, says Standish.
The form of the Jeffreys» prior depends on both the relationship of the observed variable (s) to the parameter (s) and the nature of the observational errors and other uncertainties, which determine the form of the likelihood function.
Understanding them caused one expert to joke, «The best explanation [for the Moon] was observational error — the Moon does not exist.»
You could propagate the observational error estimates provided along with the HADCRUT4 data set through this transformation, as well as use information about the distribution of a and g from individual models in the CMIP ensembles.
The form of the Jeffreys» prior depends on both the relationship of the observed variable (s) to the parameter (s) and the nature of the observational errors and other uncertainties, which determine the form of the likelihood function.