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The problem of estimating the parameter of a common distribution of components' lifetimes from system's lifetime data is of interest and importance in reliability engineering.
All these applications of WSNs are based on the same fundamental task of sampling (i.e., observing) some signal parameter using sensors geographically distributed over a field and estimating the parameter of interest using a central processing unit (fusion center).
Since current GWAS panels only focus on common variants, we are estimating the parameter of the exponential distribution based on the set of common SNPs, and hoping the result can be extrapolated to rare variants.
Applying Bayesian networks seems, at a first glance, to be a viable approach for estimating the parameter of simulation, but it requires that the observations are delineated by a directed acyclic model [ 38].
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The method of maximum likelihood estimation is used in estimating the parameters of the KGD.
"Parameters estimation" describes the methodology of estimating the parameters of the kinetic model using the Intelligent Firefly Algorithm.
An estimator is constructed for jointly estimating the parameters of interest.
Marginal maximum likelihood estimation is often used for estimating the parameters of the model.
Thus, estimating the parameters of an audio signal, which includes the frequency estimation, is considerably complex.
If the inference is made ignoring the randomness of the model selection process, then there could be severe biases in estimating the parameters of interest.
Lee, T.C., G. G. Judge, and A. Zellner (1970), Estimating the Parameters of the Markov Probability Model from Aggregate Time Series Data, (1st ed)., Amsterdam: North-Holland pagesshing Co., 254 pages.
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