Thanks for your reply, Evan. > It may make sense to have a more general Gibbs sampling > framework, but it might be good to have a few desired applications > in mind (e.g. higher level models that rely on Gibbs) to help API > design, parallelization strategy, etc.
I think I'm more interested in a general framework which could be applied to a variety of models, as opposed to an implementation tailored to a specific model such as LDA. I'm thinking that such a framework could be used in model exploration, either as an end in itself or perhaps to identify promising models that could then be given optimized, custom implementations. This would be very much in the spirit of existing packages such as BUGS. In fact, if we were to go down this road, I would propose that models be specified in the BUGS modeling language -- no need to reinvent that wheel, I would say. At a very high level, the API for this framework would specify methods to compute conditional distributions, marginalizing as necessary via MCMC. Other operations could include computing the expected value of a variable or function. All this is very reminiscent of BUGS, of course. best, Robert Dodier --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
