sample estimator造句

The variance of the importance sampling estimator is easily shown to be
This is the importance sampling estimator of p _ t \, and is unbiased.
Other useful concepts in quantifying an importance sampling estimator are the variance bounds and the notion of asymptotic efficiency.
James Barnes and David Allan further extended the bias functions with the B 3 function in to handle the concatenated samples estimator bias.
where c'( \ hat { \ theta } _ n ) is the derivative of c evaluated at the sample estimator.
It's difficult to find sample estimator in a sentence. 用sample estimator造句挺难的

Bimodal distributions have the peculiar property that unlike the unimodal distributions the mean may be a more robust sample estimator than the median.
Now, the importance sampling problem then focuses on finding a biasing density f _ * \, such that the variance of the importance sampling estimator is less than the variance of the general Monte Carlo estimate.
The performance measure commonly used is \ sigma ^ 2 _ { MC } / \ sigma ^ 2 _ { IS } \,, and this can be interpreted as the speed-up factor by which the importance sampling estimator achieves the same precision as the MC estimator.
For example, exactly identified models produce finite sample estimators with no moments, so the estimator can be said to be neither biased nor unbiased, the nominal size of test statistics may be substantially distorted, and the estimates may commonly be far away from the true value of the parameter.