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To prove Theorem 1.1, we still need a celebrated interpolation theorem of operators with change of measures.
Longitudinal studies provide necessary information on the stability and change of measures with disease activity.
To examine change of measures itself, paired t-test was used to test the changes in the MMS scores and repeated measures analysis of variance was used to test the changes in the Cs levels.
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Our transformation includes a change of measure that simplifies the distribution of the sufficient statistic conditioned on its previous value.
Utilising the EM method in conjunction with the change of measure technique, optimal and self-updating parameter estimates are obtained.
It is also found that the input impedance of Type 3.3 ear is more robust to the change of measuring condition than Type 3.4 ear.
To allow robust error estimates w.r.t. the unknown change of measure, we properly randomize the initial value of the forward process.
We propose a new method, based on an empirical Girsanov's change of measure, to identify the risk-neutral density from the observed unadjusted index returns.
To enhance the efficiency of the proposed scheme when treating more complex nonlinear systems, we then derive an iterative algorithm based on Girsanov's theorem on the change of measure, which features importance sampling.
Employing the method of change of measure, we present the pricing formulas in closed form for the most common product designs: the point-to-point and the annual reset.
The change of measure in our scheme is parametrized by two density functions.
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CEO of Professional Science Editing for Scientists @ prosciediting.com