Comment by zwaps | Hacker News Reader

Comment by zwaps | Hacker News Reader

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Different things to care about. I have worked on stats in research, start ups and at large banks. A lot of production/infrastructure related things certainly profit from the points you mention.

But 99% of statistical applications I have seen differ. For someone using math, "programmability" and the other aspects you mention are certainly higher in a language that emulates how we think about the problems. While indeed Clojure may work well for some areas in math, Julia is certainly appropriate for statistics and probability - in my opinion the best -, and finally, Python isn't very good for either.

Everything else is almost always secondary. I would also argue that Julia is absolutely fine for developing large, scalable programs, not worse than Python.

Since Clojure has no statistics / ML ecosystem afaik, I am not sure why you mention it. This is absolutely crucial to even being considered in my point. That is why R dominates statistics still, by a large margin.

> Since Clojure has no statistics / ML ecosystem afaik, I am not sure why you mention it.

I mentioned it to illustrate my point, that semantics and other aspects matter much more than syntax in many cases.

(Btw Clojure does have a ML / stats ecosystem, in part via the JVM, though certainly not as developed as Python / Julia / R. For instance, Anglican is a probabilistic programming language embedded in Clojure: https://github.com/probprog/anglican).

> For someone using math, "programmability" and the other aspects you mention are certainly higher in a language that emulates how we think about the problems.

Sure, but syntax is just about notation - semantics are much more important to achieving nearness to a mental model. If you can't express your mental model in another notation than the one you're familiar with, then you probably don't have a very deep understanding of said model.

Continuing with my example, prefix notation for matrix multiplication does not hurt _at all_ my ability to reason about linear algebra - it sometimes even clarifies it.

I also think you misinterpret what I meant by programmability, which is not the same thing as 'ease of programming' - more like how smoothly various parts of a program interact. If for the sake of syntactic sugar you've introduced a proliferation of different programming constructs with no unifying abstraction, then the other parts of the program will need to make a proliferation of case distinctions as well - that's one way to hurt programmability.

To add on what @valw said:

Clojure's ML/stats ecosystem is moving fast. Several important libraries are under construction and will mature in few months. Imho, it is worth following this year, for anyone interested in languages for ML/stats.

In addition to probabilistic programming libraries such as Metaprob and Anglican mentioned above, here are some libraries worth mentioning: https://github.com/MastodonC/kixi.stats https://github.com/generateme/fastmath https://github.com/techascent/tech.ml

Anyone have a comparison of Anglican vs Gen? According to https://github.com/probprog/anglican/blob/master/doc/devel.m... the programmable inference seems to be a feature of both.

Good question. Disclaimer: I’m in the lab that made Gen & was on the paper, so not impartial :)

Anglican is implemented in Clojure, and can be extended (by writing new Clojure code) to support new general-purpose inference engines. Creating those extensions requires an understanding of both the statistics and the PL concepts used in Anglican’s backend; you are essentially writing a new interpreter for arbitrary Anglican code.

Gen provides high level abstractions for writing custom inference algorithms for _specific models/problems_ (not entire general-purpose inference engines). Those abstractions don’t require reasoning about PL concepts like continuation-passing style, nor do they require the user to do any math by hand. Of course, since Gen is just Julia code, you can still reach in and implement new inference engines (just as in Anglican/Clojure) if you’re an expert. But I wouldn’t expect people who are not probabilistic programming researchers to do this (in either Anglican or Gen).

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