In undergrad I feared a feeling of locked-in-ness, and ditched my intention to do a PhD in math (which I think I could have done well in) partly for this reason, though it was also easier for me because I hadn’t established close ties to a particular line of research, and because I had programming background. I worked a couple of years in programming, and now I’m back in school doing a PhD in stats, because I like probability spaces and because I wanted to do something more mathematical than (most) programming. I guess I picked stats over applied math partly out of the same worry about overspecialization; I think stats has a bigger wealth of better-integrated more widely applicable concepts/insights.
Programming skills are very useful there. I am a programmer and one of my hobbies is implementing bayes stats algorithms in the literature. Do let me know if you come up with anything revolutionary.
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.
In undergrad I feared a feeling of locked-in-ness, and ditched my intention to do a PhD in math (which I think I could have done well in) partly for this reason, though it was also easier for me because I hadn’t established close ties to a particular line of research, and because I had programming background. I worked a couple of years in programming, and now I’m back in school doing a PhD in stats, because I like probability spaces and because I wanted to do something more mathematical than (most) programming. I guess I picked stats over applied math partly out of the same worry about overspecialization; I think stats has a bigger wealth of better-integrated more widely applicable concepts/insights.
I am curious: what do you plan to work on in stats?
I personally think more people should be working on efficient general sampling methods for Bayesian stats, for reasons I have written about here: http://goodmorningeconomics.wordpress.com/2010/11/16/the-promise-of-bayesian-statistics-pt-2/ .
Programming skills are very useful there. I am a programmer and one of my hobbies is implementing bayes stats algorithms in the literature. Do let me know if you come up with anything revolutionary.
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.