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The use of the logit and probit models solves the problem of OLS estimation for binary dependent variables.
This reflects a strength of DCM; the Bayesian inversion of these models solves the complex problem of estimating hidden or latent variables.
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The models solve the equations of fluid dynamics, and they do a very good job of describing the fluid motions of the atmosphere and the oceans.
Thus, the CFD models solve the Navier Stokes equations, and the general transport equations for each physical quantity.
These models solve the cardinality problem by restricting the domain X of interpretation, so that, in them, X is in a suitable sense isomorphic to the 'function space' XX.
Specifically, such models solve the heat conduction equation in order to estimate the temperature in the regolith as function of time and depth below the surface.
Such models solve the coupled continuity, momentum and energy equations and provide the densities, temperature and velocities of the major species.
The model solves the primitive equations.
The model solves the tradeoffs between investment and production capacity.
The global-scale modeling usually solves the elastodynamic equation in spherical polar coordinates, while the local-scale modeling solves the equation in Cartesian coordinates.
The aerodynamic model solves the Reynolds-averaged Navier Stokes equations with a Spalart Allmaras turbulence model.
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