An Introduction to Probabilistic Programming
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Re: An Introduction to Probabilistic Programming
#2NB updated as of two days ago, despite the 1809 title.
Re: An Introduction to Probabilistic Programming
#3nice! also interesting that they have chosen a lisp syntax for the book. note however that the book is written to be language agnostic
> It is a Lisp-like language which, by virtue of its syntactic simplicity, also makes for efficient and easy meta-programming, an approach many implementors will take. That said, the real substance of this book is language agnostic and the main points should be understood in this light.
Re: An Introduction to Probabilistic Programming
#4If you're interested in probabilistic programming and want something a little more hands-on, I recommend The Design and Implementation of Probabilistic Programming Languages http://dippl.org/ . It's an online course/textbook that gets you programming right away and makes the power of probabilistic programming immediately clear.
Re: An Introduction to Probabilistic Programming
#5If you're interested in probabilistic programming and want something a little more hands-on, I recommend The Design and Implementation of Probabilistic Programming Languages http://dippl.org/ . It's an online course/textbook that gets you programming right away and makes the power of probabilistic programming immediately clear.
thanks for this. there is also Probabilistic Models of Cognition [0] by one of the authors. I wish however that they stuck to Church language [1]
[1] http://web.stanford.edu/~ngoodman/papers/POPL2013-abstract.p...
Re: An Introduction to Probabilistic Programming
#6If you're interested in probabilistic programming and want something a little more hands-on, I recommend The Design and Implementation of Probabilistic Programming Languages http://dippl.org/ . It's an online course/textbook that gets you programming right away and makes the power of probabilistic programming immediately clear.
thanks for this. there is also Probabilistic Models of Cognition [0] by one of the authors. I wish however that they stuck to Church language [1] [0] https://probmods.org/ [1] http://web.stanford.edu/~ngoodman/papers/POPL2013-abstract.p...
I too preferred church, though I understand why the authors of dippl chose a more popular language (javascript) for their book. Church (a dialect of Scheme, which is a dialect of Lisp) also ties probabilistic programming back to its intellectual roots in McCarthy's amb operator [1].
That said, you can get pretty far with probabilistic programming in any language with decent monad support [2]. I did most of my probabilistic programming work in Scala. (You lose the ability to do really fancy inference if you go the monad route, as you can't analyze the program structure, but a lot of the time, this is fine.)
Re: An Introduction to Probabilistic Programming
#7Has probabilistic programming been shown to solve problems better than machine learning or deep learning approaches?
I remember it being pretty hyped 5-6 years ago …
Re: An Introduction to Probabilistic Programming
#8Has probabilistic programming been shown to solve problems better than machine learning or deep learning approaches? I remember it being pretty hyped 5-6 years ago …
I wouldn't contrast it with machine learning or deep learning. Probabilistic programming is focused on building languages or libraries that incorporate fundamental probabilistic building blocks, model building statements and inference strategies as first class citizens within the language. Its been remarkably successful, check out Stan, Pyro, PyMC, Tensorflow Probability, JAGS, BUGS and Turing as very successful projects that have been used to tackle challenging and diverse problems with probabilistic modeling . The more modern probabilistic programming languages are actually designed to incorporate the advances of deep learning by making it easy to embed ANNs into models by using them to parameterize random variables, Pyro and Tensorflow Probability are probably the best examples of this.
Re: An Introduction to Probabilistic Programming
#9Has probabilistic programming been shown to solve problems better than machine learning or deep learning approaches? I remember it being pretty hyped 5-6 years ago …
note that these are not exclusive. you could divide ML into a traditional statistical approach and a probabilistic one that is concerned with deriving the underlying probability distribution. probabilistic programming is kind of like a domain specific language for achieving this. there is also differential programming that works on the same principle. there are certainly industrial usages of this paradigm. look up pyro (http://pyro.ai/examples/intro_part_i.html) for ppl and jax (https://github.com/google/jax) for differential programming
Re: An Introduction to Probabilistic Programming
#10If you're interested in probabilistic programming and want something a little more hands-on, I recommend The Design and Implementation of Probabilistic Programming Languages http://dippl.org/ . It's an online course/textbook that gets you programming right away and makes the power of probabilistic programming immediately clear.
does this require stats/probability knowledge?
on that topic, can anyone recommend an online stats/probability course? I tried the coursera one by Sebastian Thrun and couldn't get far into it because the "TA" examples were unintelligible.