"estimation framework" is a correct and usable phrase in written English.
It is commonly used in scientific and academic papers to refer to a particular form of research that uses an outline and calculations to determine the outcome of an experiment. For example, "We used a Bayesian estimation framework to evaluate the accuracy of our models."
Functions f depend on the particular estimation framework, on the type of estimator and on the available information [21].
The gradient-based maximum-likelihood estimation framework RIMAX was used to estimate both specular and dense multipath components.
In the classical matched estimation framework, the performance of any unbiased estimator can be assessed by comparing its error covariance matrix with the Cramér-Rao bound.
The upper and lower limits are opened further wide on the gPROMS parameter estimation framework to allow the software make the best estimates.
With the well-known two-step procedure of Bayesian estimation framework, Bernoulli filters (BF) [19], as a sequential Bayesian estimator, propagates two posterior terms recursively.
GLAS then, is used as a sampling tool within a hybrid estimation framework to generate stratum-, stand-, anational-levelvel AGB estimates.
With model-assisted estimation, positional errors affect the model parameter estimates for the models that are used as part of the estimation framework.
The estimation framework supports nearly any alternative conclusion, including the smallest and largest estimates possible under the framework.
This study proposes a biped robot state estimation framework based on a compliant inverted pendulum model and a robust state estimator.
As the transcription rate functions depend on regulatory proteins, our parameter estimation framework based on the decomposition of a gene circuit model requires the estimate of the protein levels first.
The sample design presented in this paper allows identification of a subset of GLAS data which may be used as a simple random sample to estimate biomass, perhaps globally, with consistent measures of uncertainty under a model-based estimation framework.
