Sentence examples for measurement error distribution from inspiring English sources

I simulated measurement error using the inverse cumulative method to draw measured displacements from the measurement error distribution.

I used error standard deviations as a measure of displacement (described in [15]), by dividing the step length by the standard deviation of the measurement error distribution to produce a non-dimensional measure of displacement.

So it is necessary to propose a new DR method which can accommodate more variety of measurement error distribution.

I will also discuss strategies for incorporating prior information about the measurement error distribution and averaging over uncertainty in the distribution of the birthweights conditional on the true gestational age.

In this paper, generalized T distribution is applied to accommodate measurement error distribution, meanwhile, historical data is introduced to estimate the objective function parameters by using Particle Swarm Optimization (PSO) algorithm.

For comparison, the EKF algorithm adopts the measurement error distribution as Gaussian distribution N 1,4).

We map the intensity variations due to turbulence to variations in the probability distribution of the arrival time of the 1st photon in a laser pulse, which influences the range measurement error probability distribution.

Posterior distributions were estimated based on uninformative prior distributions (normal distributions with mean 0 and standard deviation 100 for parameter means; inverse gamma distributions with mean 1 and variance 10,000 for variance of measurement error; uniform distributions with range 0 to 100 for standard deviation of random intercepts).

εijk is the random measurement error following normal distribution.

The probability to miss an existent bias (beta error) depends on the measurement range, the allowable bias, the analytical measurement error and the distribution of the data [ 12].

Having regularized our data and then calculated the observed deviations that we fit using our SSM (Equations (1) and (2)) we can now be more concise about the measurement error and process error distributions for this particular application.