I work professionally in large-scale mcmc projects and don’t find quasi mc to be much more than a curiosity in my work, because integration (as opposed to drawi

I work professionally in large-scale mcmc projects and don’t find quasi mc to be much more than a curiosity in my work, because integration (as opposed to drawing a rich set of posterior samples) is virtually never the goal. To do anything of use, you have to draw samples and then simulate complex outcomes, very few of which reduce to simple averages over the drawn samples or functions of the drawn samples. And since you need to draw samples, you might as well use those samples for any parts that do happen to require integration, rather than a quasi mc calculation, except in extremely toy-problem situations where the convergence rate matters disproportionately for that smallset of outcomes. I agree that for cases when you just want to evaluate an integral it could be useful. I have never encountered a use case when anyone just wanted to calculate an integral, as opposed to also generating posterior uncertainty metrics, posterior test statistics for posterior predictive checking, posterior diagnostics like ordinal statistics among the posterior samples. I’m sure outside of stats, there must be use cases. Just adding a counterpoint to the idea that quasi mc should always be interesting to practitioners. For a lot of people who work in mcmc methods, quasi mc is just not interesting and generally speaking could never be.