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.*
*IllegalArgumentException No value supplied for key: 0.7
 clojure.lang.PersistentHashMap.createWithCheck (PersistentHashMap.java:89)*

I am a total beginner with Clojure, if you could provide a at least a hint
of how to resolve this - I'd appreciate it.

P.S. I am using the "1.3" branch by Jeff, that works with Leiningen.

Thanks!

- Julius

On Sun, Nov 20, 2011 at 6:32 AM, Jeff Rose <ros...@gmail.com> 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 sampling, but if I understand it
> correctly, your grass-is-wet demo is defining a belief network where
> each sample taken represents the complete state of the graph, or just
> the final outcome?  What does a sample look like?  It would be great
> if we could use this kind of generative model to create chord
> sequences, melodies, and rhythms for Overtone.  I don't know what
> kinds of choice points would be appropriate, or if we could train them
> based on a database of existing progressions?
> -Jeff
> On Nov 18, 12:57 am, Nils Bertschinger
> <nils.bertschin...@googlemail.com> wrote:
> > 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/ProbClojureNice.
> >
> > The monadic version is more a proof of principle and not very fast. It
> > might nevertheless be useful, e.g. for educational purposes. Have a
> > look and decide for yourself ...
> > For the future, I'm working on a different approach to embed
> > probabilistic operations into clojure which scales better and allows
> > to run somewhat larger models.
> >
> > Any comments and feedback are welcome. Best,
> >
> >     Nils
>
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