The phrase "allows for convergence" is correct and usable in written English.
It can be used in contexts where you want to indicate that a particular situation or condition enables different elements to come together or reach a common point.
Example: "The new policy allows for convergence of various departments, fostering better collaboration and communication."
Alternatives: "facilitates convergence" or "enables convergence".
In general, then, the symmetric analysis of similarity, combined with the de facto asymmetry of overdetermination, implies that worlds that accommodate counterfactual changes by preserving the actual past and allowing for divergence miracles are more similar to the actual world than worlds that accommodate such changes by allowing for convergence miracles that preserve the actual future.
Similarly, the complexity of brute force version of the IAA spectral estimator is provided in[47] as C IAA = m c [ 2 K N o 2 + K N o + N o 3 ], where m c is the number of IAA iterations necessary to allow for convergence, and K and N o are the frequency grid size and the number of observed data samples.
Our analyses employed a long burn-in period (50 000 iterations) and follow-up period (80 000-100 000 iterations) to allow for convergence.
This number of MCMC generations allowed for convergence of simulation chains and reduction of split frequencies to an acceptable level.
The initial 5,000 iterations were discarded to allow for convergence and every hundredth of the following 500,000 iterations were sampled for the posterior distribution.
Furthermore, using the directly combined axial scans acts as a good initial solution given that it allowed for convergence to the global minima for our test cases.
All the Bayesian analyses were performed using 3 chains of 50000 samples, out of which the first 25000 were removed to allow for convergence.
Although both the theta model and the control model are intrinsically stochastic, the sample size was large enough for the objective function over the entire sample to be nearly deterministic, allowing for convergence of the gradient descent algorithm.
The case of no interior switch points corresponds to, that is the solution of a single linear ODE from an initial condition, whereas each additional switch-point adds another additive term allowing for convergence to a new equilibrium.
It should be noted that because Bayesian analysis is typically realized by Markov Chain Monte Carlo (MCMC) sampling, which relies on prior distributions and initial sampling values being sufficiently reasonable to allow for convergence of the posterior distribution, there is a limit to how non-informative a prior can feasibly be.
