Quasi-Monte-Carlo vs. variable dimensionality?
<p>I've been looking through the Matlab documention on using quasi-random sampling of N-dimensional unit cubes.  This represents a problem with N stochastic parameters.  Based on the fact that it is a unit cube, I presume that I need to use the inverse CDF of each parameter to map from the [0,1] domain to the value range of each parameter.</p>

<p>I would like to try this on a problem for which I now use Monte Carlo.  Unfortunately, the problem I'm analyzing does not have a fixed number of dimensions.  For each instantiation of the problem, I generate a variable number of widgets (say) using a Poisson distribution.  Only after that do I randomly generate the parameters for each widget.  That whole process yields one instance of the problem to be analyzed, so the number of parameters varies from one instance to the next.</p>

<p>Is this kind of problem still amenable to Quasi-Monte-Carlo?</p>