Sentence examples for asymmetric problems from inspiring English sources

The tree-based chain event graph (CEG) has been developed specifically for such asymmetric problems.

We present the Chain Event Graph (CEG) as a complementary graphical model to the Causal Bayesian Network for the representation and analysis of causally manipulated asymmetric problems.

Its characteristics include the following: it is applicable to both symmetric and asymmetric problems with random elements; it does not use subjective decisions, so it can be completely mechanized; it is appreciably faster than any other method proposed; and it can be terminated at any point where the solution obtained so far is deemed sufficiently accurate.

Our work again is to study asymmetric problem (1.1) without the (AR -condition in the positive semi-AR -condition

Thus, it is obvious that, to a certain extent, the proposed controller overcomes the asymmetric problem and obtains more consistent control performance across the scope of work.

The asymmetric problem arose from the lack of information on the borrower's ability and willingness to repay the loans and thus an inability to separate safe borrowers from risky types.

(SCE):f has subcritical (exponential) growth on Ω, i.e., lim t → ∞ | f ( x, t ) | exp ( α | t | N N − 1 ) = 0 uniformly on x ∈ Ω for all α > 0. When p = N and f has the subcritical (exponential) growth ( S C E ), our work is still to study asymmetric problem (1.1) without the (AR -condition in the positive semi-AR -condition

To illustrate the above-mentioned approach, the well-known 0 1 knapsack problem, asymmetric travelling salesman problem and 2-constraint bin packing problem are considered.

We find that (i) contrary to existing results, step-up bonds are indeed able to mitigate the asset substitution problem, (ii) the use of a step-up feature can be a credible signal to overcome asymmetric information problems, and (iii) the optimal design as well as the conditions for the optimal use of step-up bonds is considerably different for the two explanations.

We also compare CEGs with Influence diagrams, Valuation Networks, Sequential decision diagrams, Sequential influence diagrams and Decision circuits for the representation and analysis of asymmetric decision problems.

In particular, we look at the representation of asymmetric decision problems including conditional distribution trees, sequential decision diagrams, and sequential valuation networks.