Biagini and Bion-Nadal (2014) study dynamic performance measures in a fairly general setup that generalize the results of (Bielecki et al. 2014b).
In this paper, we consider a class of fairly general sampling algorithms that generate the partial observation (and maybe sparse).
The Gibbs measure is assumed to be generated by a fairly general Hamiltonian function with pair interaction.
We will show, in a fairly general Banach space, that the sequence,, generated by the above formulas converge weakly to the common fixed point of, respectively.
We will show, in a fairly general Banach space, that the sequences,, and generated by (1.13), (1.14), and (1.15) converge weakly to the common fixed point of, respectively.
Our general mathematical approach will allow our findings, while made in a model for turtle motor rhythm generation, to be extensible to other networks with fairly general features.
A fairly general contest model is presented.
The restoration there is fairly general.
These results are fairly general and can be easily verified.
We show that this holds for both intensive and extensive margin responses under fairly general conditions.
Although the treatment is fairly general, we will use the glucose system as a prototype.
