[Haskell-cafe] Comment on "The Historical Futurism of Haskell" by Andrew Boardman

[Dominik Schrempf]

Henning Thielemann <lemming at henning-thielemann.de> writes:

> On Sun, 12 Sep 2021, Dominik Schrempf wrote:
>
>> I work with Markov chains (yes, I had to write my own MCMC library in order to
>> run proper Markov chains),
>
> I don't know what proper Markov chains are, at least I wrote my own simple lazy
> Markov chain generator years ago that fulfilled my needs:
>    https://hackage.haskell.org/package/markov-chain
>
> I have also written a package for Hidden Markov models:
>    https://hackage.haskell.org/package/hmm-lapack

Thank you for mentioning this. I was playing around with 'markov-chain' some
time ago! I was referring for something more generic, for example, where the
transition rate/probability matrices can be directly defined. I am not so
familiar with hidden Markov models. With respect to the term "proper", thanks
for picking that up :); I should have been more specific: I was referring to
Markov chain Monte Carlo samplers. For a reference implementation, please see
the 'mcmc' library in R [1], for a feature-rich one, e.g., 'PyMC3' [2].

>
>> I need to plot my data (there is no superb standard plotting library available
>> in Haskell). By now, I do maintain library packages providing answers to some
>> of these problems, but it was (and is) a lot of work.
>
> If you write packages that fulfill the needs of your applications, that's
> certainly better than writing impressive frameworks where no one knows 
> whether it is usable, at all. :-)

You are right, we want to avoid impressive but unusable frameworks. I was
referring to the following question: "For a given goal, which features do I have
to implement myself in order achieve the goal?" With the existing set of Haskell
libraries, I make the following observation too often (or too early in my stack
of requirements): "This feature is not available (or not readily available), I
need to implement it myself." For example, at the moment I am working on a
library for estimating covariance matrices from sample data. This is not at all
my area of expertise. In Python and R, well maintained state-of-the-art methods
are readily available (e.g., shrinkage based estimators such as the Ledoit-Wolf
estimator, or oracle approximating shrinkage, as well as estimators based on
Gaussian graphical models such as the graphical lasso --- actually, there is a
glasso library on Hackage but it seems unmaintained, lacks documentation, and
improvements from newer findings in the last 10 years).

I hope I made myself clear. I don't want to naysay. I really enjoy Haskell per
se! I am a big fan.

Dominik

[1] https://mran.microsoft.com/snapshot/2015-02-27/web/packages/mcmc/index.html

[2] https://docs.pymc.io/