If you are doing particle event simulation simply using forward inference, then map-only tasks are just fine and almost anything will do. Mahout has decent random number generators.
If you want modern MCMC codes for reverse inference, I would go elsewhere. Radford Neal has some good stuff and Andrew Gelman's group has been doing some good work. Both of these are ultimately accessible from R. On Thu, Oct 27, 2011 at 7:03 AM, Charles Earl <[email protected]> wrote: > Hi Ted, > This is mostly for particle event simulation, I'll give the FOAM toolkit as > an example > http://jadach.home.cern.ch/jadach/Foam/Index.html > But I'm trying to determine if there might be commonalities for other > domains, options pricing as one. > Yes, you are right about GNU SL. I have also wonder whether Colt or > Parallel Colt might be worth investigating. > Thanks > Charles > > On Oct 26, 2011, at 5:47 PM, Ted Dunning wrote: > > > We have no significant monte carlo methods in Mahout. > > > > We do have a reasonable evolutionary optimizer, but randomness isn't the > > same as a real Monte Carlo code in the sense of Metropolis-Hastings or > Gibbs > > sampling. > > > > What do you mean by "capability of GNU scientific computing library"? I > > don't know of any significant MCMC code there either. > > > > In general, efficient MonteCarlo codes can be tricky to get to work in > the > > map-reduce paradigm. You can start multiple chains, but that doesn't > > necessarily help if all have to spend a long time burning in. > > > > Can you say more about what you are looking for? > > > > On Wed, Oct 26, 2011 at 2:34 PM, Charles Earl <[email protected]> > wrote: > > > >> I'm interested to learn if there is a general monte carlo methods > package > >> that has been developed for Mahout or Hadoop. For example, having the > >> capability of GNU scientific computing library. This might be off the > >> machine learning focus of Mahout, but thought there might be overlap. > >> Thanks. > >> Charles Earl > >> > >> > >
