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Both methods have desirable accuracy.
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Students graduate this program with a broad set of skills, from the ability to interact collaboratively with researchers in applied fields, through the formulation and computational implementation of novel statistical models and methods to demonstrating mathematically that these methods have desirable statistical properties.
The TLISMNI method has desirable features that include avoiding numerical differentiation of the forces, allowing for an efficient sparse matrix implementation, and ensuring that the kinematic constraint equations are satisfied at the position, velocity and acceleration levels.
We also show that our method has desirable properties (i.e., efficiency and robustness).
Both methods have their problems.
The panel said both methods have a role.
Both methods have run into roadblocks.
Both methods have their pros and cons.
Both methods have merit.
Both methods have several drawbacks.
Both methods have known limitations.
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