nipun_batra

Good questions. A few quick thoughts. These may not be enough though!

First, the mean function specified in the prior is zero for all the points. Thus, points would have a high density near zero.

I have a strong hunch that choosing a "smoother" kernel would not lead to this discontinuity.

I agree that distill.pub is great and so is the article you've mentioned. I did seek inspiration from the linked article amongst many others.

Many thanks for your generous comment :)

Very interesting! Thanks for the comment! I do see the advantages of beaker. I also see the advantages other way around :)

I do agree that stats people love R. This is one of the reasons I wrote this article. So that I can leverage all the good work they've gone. It is way better to write a wrapper than to reimplement everything from scratch. I'm coming from academia where I have a lot of systems work. So, I tend to be Python first user. But, I do want to utilise all the packages that are exclusively available in R.

Packaging is improving in Python afaik. People like Anaconda!

Language wars again :) I think both languages have their own strengths. I come from a programming background and took to Python. However, I often come up in situations when there are R implementations to some advanced statistical routines and none exist for Python. I'm sure vice versa would also be true. So, this is an attempt from that angle :)

I wonder if a fully automatic translator would ever be feasible. The current approach of handwriting the wrapper code for the packages I'm interested in seems to work well. I guess with a bit of community support, the recipe set can become richer.

I'll second that R is really awesome! Being a computer scientist, it opens up for me a lot of statistical packages.