# estimation theory

(redirected from Estimating Parameters)

## estimation theory

[‚es·tə′mā·shən ‚thē·ə·rē]
(statistics)
A branch of probability and statistics concerned with deriving information about properties of random variables, stochastic processes, and systems based on observed samples.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
After estimating parameters a and b, we provided evidence to demonstrate the appropriateness of our Ne estimation method.
When both estimating parameters and forecasting future values, it is extremely important to use a model that will not systematically deviate from observed deformation indicator values.
The first stage includes estimating parameters of marginal distributions and afterwards the parameters in copula.
We employ SMM by matching a set of empirical moments with simulated moments, estimating parameters from two models: 1) the base case model and 2) the payout consistency cost model.
Among the topics are fundamental concepts, estimating parameters, classification, unfolding, constrained fits, how to deal with systematic uncertainties, analysis walk-throughs, and applications in astronomy.
Although Model (2) fit the data well and had a fairly low [DELTA]AIC (14.82), it can attain negative mass values, a fact that is troublesome both theoretically and numerically when estimating parameters. Although Hoyt's Model (1) lags well behind the first 3 models, it still explains 87% of the variability in the estimation data with only 1 species-specific parameter.
Such representation of the covariance matrix significantly simplifies the process of estimating parameters. The estimation procedure is done using the maximum likelihood method.
The first section sets out fundamentals such as the relations between the latent variable and its indicators, and estimating parameters and selecting models.
Furthermore, common methods for estimating parameters, including conjugate gradient descent and expectation maximization, suffer from finding local maxima that are heavily dependent on their starting values.

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