Estimation theory
Branch of statistics to estimate models based on measured data / From Wikipedia, the free encyclopedia
Dear Wikiwand AI, let's keep it short by simply answering these key questions:
Can you list the top facts and stats about Estimation theory?
Summarize this article for a 10 year old
SHOW ALL QUESTIONS
"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.