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From Charles Earl <>
Subject Re: Monte Carlo toolkits in Mahout?
Date Thu, 27 Oct 2011 17:39:35 GMT
I have read R has some collab with Clodera. Is there a "mapreduce R" that is stable (apart
from streaming).

On Oct 27, 2011, at 10:16 AM, Ted Dunning <> wrote:

> 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 <> wrote:
>> Hi Ted,
>> This is mostly for particle event simulation, I'll give the FOAM toolkit as
>> an example
>> 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 <>
>> 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

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