Sentence examples for models we prove from inspiring English sources

With these models we prove the convergence of human behavior to the observed aggregate decision making for reward structures with matching points.

After observing that gamma distributions are only reversible distributions for this class of models, we prove that every generalized gamma convolution is a stationary distribution of the process with suitably chosen branching mechanism and with continuous immigration.

In a generic setting of Wess Zumino models, we prove that the existence of a supersymmetric vacuum with a vanishing superpotential can be a consequence of a continuous or discrete R-symmetry when invariant fields are not less than fields transforming in the same way as the superpotential under the R-symmetry.

By reducing our task to the setting of graphical models, we prove that the method finds a near-optimal design selection with a polynomial number of evaluations.

Using GSH detection as a model, we prove a linear dose response in the range from 5×10−10 to 8×10−8 M.

By using the Canetti Krawczyk security model, we prove that the protocol is secure with PFS under the Computational Diffie–Hellman assumption in the random oracle model.

By clarifying the mathematical properties and solution space of the model, we prove that the MSBT problem is NP-hard, and its Pareto-optimal front is non-convex.

For a one-way mixed Gaussian ANOVA model we prove local asymptotic normality and local asymptotic minimaxity of maximum likelihood estimates (MLE) and of its certain iterative approximations.

Under a general traffic model, we prove that our scheme always outperforms the baseline scheme in terms of transmission cost while satisfying accuracy and real-time requirements.

Specifically, in a white noise model as well as a fixed-design regression model, we prove a Bickel Rosenblatt-type theorem for the maximal deviation of a kernel-type estimator from its mean, and give uniform estimates for the Bickel Rosenblatt-typev smootheoremclass.

In the case of the Kac model, we prove that for every ε>0, if F has moments of every order and finite Fisher information, there is a constant C so that for all n, ∥Q+n(F −M∥L1(R)⩽CnΛ+ε where Λ is the least negative eigenvalue for the linearized collision operator.