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I'm used to MCMC's for statistics problems, and it seems to me the authors are being too optimistic about its feasibility as a search strategy. Also, from a te
by mturmon 13y ago
I'm used to MCMC's for statistics problems, and it seems to me the authors are being too optimistic about its feasibility as a search strategy.
Also, from a technical point of view, it seems like they started out with an optimization problem on a hard domain, and then made their problem even harder by using a sampling algorithm with stochastic moves (i.e., MCMC). The particular usefulness of MCMC is sampling, not optimization, and for this problem, you don't really want to sample.
- pavpanchekha 13y agoAs per the evaluation, STOKE works great. So if they made a hard problem harder, they clearly didn't make it too hard.
- mturmon 13y agoLet's stipulate that it worked great on the problem they analyzed. That problem was really very simple. If they used a more appropriate technique, they could expand the domain of applicability significantly. Like I said, I've used MCMC for loosely-structured inference problems (but more structured than the one treated in the paper). I was doing MAP estimation (i.e., maximizing the objective function), just as in the paper. When speaking about my work, I was once asked "Why MCMC on such a hard problem if you're really just maximizing?" It's still a good question.