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P t,t−1) is the one-step prediction of the matrix of error covariance at moment t.
9 Due to the criterion of additivity, the variance‐covariance matrix of error terms for a complete equation demand system will be singular.
Secondly, with the matrix of error covariance and observability of the system, this paper designs a vector-form information sharing algorithm so that each state variable can get a different coefficient.
We use the average absolute value of differences between true and inferred frequencies as a matrix of error.
Here Y is the J × n matrix of observed blood glucose measurements; Φ is the J × K matrix of the values of the K basis functions evaluated at times t j, and the J × n matrix of error terms.
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The problem studied in the paper concerns the estimation of individual weights of p objects according to the model of an A-optimal chemical balance weighing design with a positive definite diagonal variance matrix of errors under the restriction p1+p2= q⩽p, where p1 and p2 represent the numbers of objects placed on the left and on the right pan, respectively, in each of the measurement operations.
The standard unadjusted EWAS analysis (on beta values) posits the linear model (1) where is an matrix of coefficients and is an matrix of errors.
The rows of the n × p matrix of errors, E, are assumed to be independent and identically distributed draws from a multivariate normal distribution.
where P, Q and R are the coefficient matrices of error vectors E j + 1, E j and E j − 1, respectively.
With the help of (7.7c) and (7.7d), we obtain boldsymbol{phi} (mathbf{w}) - boldsymbol{phi} (mathbf{W}) = mathbf{PE}^{j + 1} + 2mathbf{QE}^{j} + mathbf{RE}^{j - 1}, (7.8) where P, Q and R are the coefficient matrices of error vectors (mathbf{E}^{j + 1}), (mathbf{E}^{j}) and (mathbf{E}^{j - 1}), respectively.
Let us define the second central moment of the error vector as and the covariance matrix of the error vector as.
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