What are your thoughts on slpKanren? Could it be used as a base for 
probabilistic programming in core.logic?
https://github.com/webyrd/slpKanren

- Martin

Den onsdagen den 24:e april 2013 kl. 17:34:05 UTC+2 skrev David Nolen:
>
> This is great, thanks for the thoughts, gist & links!
>
>
> On Wed, Apr 24, 2013 at 5:34 AM, Zack Maril <thewi...@gmail.com<javascript:>
> > wrote:
>
>> Lately, I've been on a bit of a jag into probabilistic programming with 
>> Clojure, specifically embedding Church inside of Clojure. The results so 
>> far are promising from a syntactic level, but, like David said, getting it 
>> to actually work is another matter entirely. I wanted to share what I've 
>> been able to get working so far and some of the potential challenges of 
>> embedding Church in Clojure. 
>>
>> https://gist.github.com/zmaril/5447488
>>
>> The above gist is a self contained Clojure program that implements, among 
>> other things, `query-by-rejection` and `flip`. With these two functions, we 
>> can already do most of what Church seems to do. What's missing from 
>> a functionality standpoint is support for various distributions and some 
>> useful functions related to tolerance (and, of course, a good MCMC/Gibbs 
>> implementation). What's been gained is, via some careful macro writing, the 
>> ability to reuse code, specifically to reuse memoized functions. 
>>
>> One of the key ideas behind Church is that memoization allows one to 
>> express complicated scenarios very concisely. So, to code up a randomized 
>> persistent trait (like a person's eye color), you simply define a memoized 
>> function that takes in a person and returns their eye color. Every time a 
>> new world is generated, the memoized function gets recreated. But within 
>> the world (or current experiment), the trait persists and can be referenced 
>> again in various places without too much hassle.  Note that a new memoized 
>> function must be created for each experiment, i.e. you can't just memoize 
>> the function outside the query and bring that back in. Within the gist 
>> above, binding is used to carefully rebind any function provided in the 
>> :memobound clause for each experiment. By declaring a var to be dynamic, we 
>> can write queries that are pretty short but all rely on the same logic. 
>> From a syntactic standpoint, it took about one evening of work to cut down 
>> the length of most of the Church examples by at least half. 
>>
>> From a speed standpoint, Church is way, way ahead of the above. Sampling 
>> via rejection is quite slow compared to modern methods like MCMC or Gibbs. 
>> It might not even be possible to do the dynamic rebinding of memoized 
>> functions mentioned above and get as fast as Church is. I really don't 
>> know. Here's one of the first papers on Church:
>> http://www.stanford.edu/~ngoodman/papers/churchUAI08_rev2.pdf
>>
>> The paper is about five years old now, but section 4.1 goes into how 
>> Church was first implemented with a MCMC. The key idea they introduce here 
>> is the computation trace. I won't try to summarize it here because I don't 
>> fully understand it yet. If it means what I think it means though, then it 
>> should be possible to build and keep track of the computation trace thanks 
>> to the JVM and Clojure. My intuition says that a very dedicated student 
>> could probably produce a Clojure library to catch Church in terms of speed 
>> by the end of the summer, simply by emulating what they have done and 
>> letting pmap take care of the rest.  
>> -Zack
>>
>> On Wednesday, April 24, 2013 12:48:56 AM UTC+4, David Nolen wrote:
>>
>>> On Tue, Apr 23, 2013 at 2:10 PM, Radosław Piliszek 
>>> <radzi...@gmail.com>wrote:
>>>
>>>> 1) Is this place the best to discuss this?
>>>>
>>>
>>> Yes.
>>>  
>>>
>>>> 2) Are there some set goals that CLP(Prob) should achieve? (,,Basic 
>>>> support of CLP(Prob).'' does not express it too well! :-P )
>>>>
>>>
>>> This seems like a pretty challenging one as there are a variety of 
>>> possible approaches. Basic support for CLP(Prob) could very well mean 
>>> *several* prototypes. That said the probabilistic Prolog variants are 
>>> probably worthy of the most study as core.logic is closest to that model.
>>>  
>>>
>>>> 3) Is there any API sketch that should be followed? Is it still yet to 
>>>> be discussed? And, most importantly, how would you see CLP(Prob) fit in 
>>>> core.logic's ecosystem?
>>>>
>>>
>>> There is no API sketch. It's extremely important to survey the links, 
>>> try out existing implementations, assess their advantages / disadvantages 
>>> and devise a syntax (or several) that works reasonably well with what we've 
>>> already established in core.logic. 
>>>
>>> Of the projects listed this is probably the most experimental and 
>>> research-y. I think if anyone seriously wants to take this on they have to 
>>> be extremely focused / self-directed and be willing to put in a 
>>> *considerable* amount of time. I'm of course willing to help in whatever 
>>> way I can as far as implementation & integration approach - but it will be 
>>> a big learning experience for me as well!
>>>
>>> David
>>>
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