stochastic matrices造句

例句与造句

1. The product of two right stochastic matrices is also right stochastic.
2. Multiplying together stochastic matrices always yields another stochastic matrix, so "'Q "'must be a stochastic matrix.
3. That is, the Birkhoff polytope, the set of doubly stochastic matrices, is the convex hull of the set of permutation matrices.
4. Multiplying together stochastic matrices always yields another stochastic matrix, so "'Q "'must be a stochastic matrix ( see the definition above ).
5. *PM : there are no non-square doubly stochastic matrices, id = 6938-- WP guess : there are no non-square doubly stochastic matrices-- Status:
6. It's difficult to find stochastic matrices in a sentence. 用stochastic matrices造句挺难的
7. *PM : there are no non-square doubly stochastic matrices, id = 6938-- WP guess : there are no non-square doubly stochastic matrices-- Status:
8. The probabilistic automaton replaces these matrices by a family of stochastic matrices P _ a, for each symbol a in the alphabet \ Sigma so that the probability of a transition is given by
9. A common convention in English language mathematics literature is to use row vectors of probabilities and right stochastic matrices rather than column vectors of probabilities and left stochastic matrices; this article follows that convention.
10. A common convention in English language mathematics literature is to use row vectors of probabilities and right stochastic matrices rather than column vectors of probabilities and left stochastic matrices; this article follows that convention.
11. He obtained a tightening of the Birkhoff von Neumann theorem with H . K . Farahat stating that every doubly stochastic matrix can be obtained as a convex combination of spectra of doubly stochastic matrices.
12. The Birkhoff von Neumann theorem says that every doubly stochastic real matrix is a convex combination of permutation matrices of the same order and the permutation matrices are precisely the extreme points of the set of doubly stochastic matrices.
13. Each state vector should be imagined as specifying a point in a simplex; thus, this is a topological automaton, with the simplex being the manifold, and the stochastic matrices being linear automorphisms of the simplex onto itself.
14. In particular, the state of a probabilistic automaton is always a stochastic vector, since the product of any two stochastic matrices is a stochastic matrix, and the product of a stochastic vector and a stochastic matrix is again a stochastic vector.
15. In doing so, the paper has provided a generalization of the Birkhoff-von Neumann Theorem ( a mathematical property about Doubly Stochastic Matrices ) and applied it to analyze when a given random assignment can be " implemented " as a lottery over feasible deterministic outcomes.
16. A key result in the case of linear-quadratic control with stochastic matrices is that the certainty equivalence principle does not apply : while in the absence of multiplier uncertainty ( that is, with only additive uncertainty ) the optimal policy with a quadratic loss function coincides with what would be decided if the uncertainty were ignored, this no longer holds in the presence of random coefficients in the state equation.