Branch of statistics to estimate models based on measured data
"Parameter estimation" redirects here. Not to be confused with Point estimation or Interval estimation.
For other uses, see Estimation (disambiguation).
Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data. An estimator attempts to approximate the unknown parameters using the measurements.
In estimation theory, two approaches are generally considered:[1]
The probabilistic approach (described in this article) assumes that the measured data is random with probability distribution dependent on the parameters of interest
The set-membership approach assumes that the measured data vector belongs to a set which depends on the parameter vector.
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Walter, E.; Pronzato, L. (1997). Identification of Parametric Models from Experimental Data. London, England: Springer-Verlag.
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