As the discovery is made through the KauNet triggers there is no need to run an Announce mechanism on node B. Node B only has to define an interface that creates a standard TCP convergence layer.
By a standard weakly convergence argument, we can conclude that the sequence { v t k N } N = 1 ∞ possesses a subsequence convergent to a vector function v t k in H m, 0 ( − γ, Ω h ∞ ).
A further 300,000 samples were then drawn, and standard convergence tests applied [46].
Convergence of the methods is shown through standard convergence analysis.
However, the standard convergence tests in the economic growth literature (i.e., β− and σ−convergences) applied in Barros [6] and Nixon [50] assume that all countries follow the same growth path.
Convergence results for the scheme (i), in the case in which { ρ k } is contained in a compact subset of ( 0, 1 ), can be found in [2]; the convergence analysis of the double backward scheme given by (ii), which can be found in [3] and [4]; the standard convergence analysis for (iii) one can see [5].
When more agents are present in the scenario, as it is the case in our setting, the standard convergence proof of time difference algorithms does not hold anymore, as the Markov transition models depend also on the unknown policies of the other learning agents.
Although the model space is discrete and finite, standard convergence diagnostics over the full model space are difficult to apply in practice.
As a standard analysis of the convergence properties does not appear to be tractable, we provide a numerical evaluation of convergence in a practical scenario in section 4. The sequential estimator is summarized in Algorithm 1.
By a standard theorem on the convergence of Feller processes (cf. [20], Thm.
