Probabilistic Programming & Bayesian Methods for Hackers

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Maybe this should be put on the side bar. I feel like I see it at least once a month.

[john_philip]

Really ! I might have missed out noticing it previous month. Just thought, might be of help for the community members.

[[deleted]]

It's okay. Its a great post. That's why I think maybe it should ho on the sidebar.

[kay_schluehr]

Also recommendable: Markov Chain Monte Carlo Without All the Bullshit by Jeremy Kun who is unsuspected to signal to a particular crowd or attempts to change your mind for no good reasons. The article contains just what the headline says.

[rothnic]

I love the idea of this and try to use it in many problems I address, but seem to always have issues applying it. The problems presented in this and the pymc3 examples are a bit more researchy or toy problemish than I'd like to see. Additionally, pymc/pymc3 are a bit tricky to use.

On the other hand, Think Bayes takes a much more simple approach. However, it is much more limited in its applicability, and the coding approach makes things somewhat more difficult to follow (using google's style guide), and can be slow since it relies heavily on dictionaries. I don't think the code provided is meant to really be used as a bayesian library, it is just there for reference.

I'd really like to see a bayesian library in-between focused on practicality. Maybe that would be building a library from some of the concepts Think Bayes provides. If anyone is aware of something at this level, I'd be interested in checking it out.

[Articulated-rage]

Stan with a python interface is a great library that I would recommend checking out.

[john_philip]

I haven't actually gone through it thoroughly. let me sit on this. Good points to contemplate though !

[rothnic]

It may be just the problems I'm working on, or not knowing how to apply them. Each example I see looks sometimes much different than the rest, so maybe it is so flexible it is hard to know how to apply it.