Hi Julius,
good catch, I don't know how I missed that???
Just uploaded a fix ... should work now.
Nils
On Nov 20, 5:58 pm, Julius Seporaitis wrote:
> Hello guys,
>
> I would like to try out this library, but ran into a problem with Clojure
> 1.3, 'lein repl' throws an exception, when:
>
> *user
Hi Jeff,
good idea to move this over to clojure 1.3. I have just included your
patch.
On Nov 20, 5:32 am, Jeff Rose wrote:
> Cool! I experimented a little bit with Church a while back, but
> having something like this in Clojure could be really interesting. I
> don't have much experience with
Hello guys,
I would like to try out this library, but ran into a problem with Clojure
1.3, 'lein repl' throws an exception, when:
*user=> (use 'probabilistic-clojure.monadic.demos)*
*user=> (test-mixture mixture-mem)*
*Trying to find valid trace ...*
*Starting MH-sampling.*
*Illeg
Cool! I experimented a little bit with Church a while back, but
having something like this in Clojure could be really interesting. I
don't have much experience with sampling, but if I understand it
correctly, your grass-is-wet demo is defining a belief network where
each sample taken represents t
On 19/11/11 00:32, Nils Bertschinger wrote:
downstream conditioning somewhat different. The stream can basically
be filtered to implement rejection sampling, whereas I thread a
database state through the program to record all random choices (as
well as their probability) that have been taken.
On Nov 18, 8:05 am, Konrad Hinsen
wrote:
> --On 17 novembre 2011 15:09:11 -0800 Nils Bertschinger
>
> wrote:
> > The two approaches are somewhat complementary to each other. Your
> > monad does exact inference on discrete distributions by running
> > through all possibilities. Mine is sampling ba
--On 17 novembre 2011 15:09:11 -0800 Nils Bertschinger
wrote:
The two approaches are somewhat complementary to each other. Your
monad does exact inference on discrete distributions by running
through all possibilities. Mine is sampling based and does approximate
inference using MCMC.
I tried
Hi Konrad,
thanks for the link. I know your very nice library and actually use
clojure.contrib.monads to implement my probability monad.
The two approaches are somewhat complementary to each other. Your
monad does exact inference on discrete distributions by running
through all possibilities. Mine
On 17 nov. 2011, at 18:57, Nils Bertschinger wrote:
> inspired by the bher compiler for the probabilistic scheme dialect MIT
> Church, I have implemented a version of the probability monad which
> uses Metropolis Hastings to draw samples from runs of monadic
> programs. You can find the code on gi
Hi everyone,
inspired by the bher compiler for the probabilistic scheme dialect MIT
Church, I have implemented a version of the probability monad which
uses Metropolis Hastings to draw samples from runs of monadic
programs. You can find the code on github:
https://github.com/bertschi/ProbClojureN
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