Currently I’m taking classes and working on a polytope sampler. I tend to be excited about Bayesian nonparametrics and consistent families of arbitrary-dimensional priors. I’m also excited about general-purpose MCMC-like approaches, but so far I haven’t thought very hard about them.
It’s just a vanilla (MH) MCMC sampler for (some convenient family of) distributions on polytopes; hopefully like this: http://cran.r-project.org/web/packages/limSolve/vignettes/xsample.pdf , but faster. It’s motivated by a model for inferring network link traffic flows from counts of in- and out-bound traffic at each node; the solution space is a polytope, and we want to take advantage of previous observations to form a better prior. But for the approach to be feasible we first need to sample.
Currently I’m taking classes and working on a polytope sampler. I tend to be excited about Bayesian nonparametrics and consistent families of arbitrary-dimensional priors. I’m also excited about general-purpose MCMC-like approaches, but so far I haven’t thought very hard about them.
What is a polytope sampler? Link to work?
It’s just a vanilla (MH) MCMC sampler for (some convenient family of) distributions on polytopes; hopefully like this: http://cran.r-project.org/web/packages/limSolve/vignettes/xsample.pdf , but faster. It’s motivated by a model for inferring network link traffic flows from counts of in- and out-bound traffic at each node; the solution space is a polytope, and we want to take advantage of previous observations to form a better prior. But for the approach to be feasible we first need to sample.
But this is not a long-term project, I think.
It seems like you might want to check this guy’s work out.
Looks like good stuff … thanks for the tip.