
A Python tutorial on Bayesian modeling techniques - gedrap
https://github.com/markdregan/Bayesian-Modelling-in-Python
======
markdregan
I am the author of this tutorial. If you are interesting in contributing a
section to this tutorial, please get in touch. Some suggested topics: survival
analysis, mixture models, classification, time series models... Twitter
@markdregan

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kriro
Looks pretty nice, good job. I think survival analysis is a very underrated
tool. I'm working in UX now and there's a lot of test setups were survival
analysis makes a lot of sense but isn't used (mothly because people don't know
it).

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lookACamel
Would you like to expand on that?

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colund
It's a good initiative!

However, I think the introduction could be improved by briefly describing the
"why/what" of Bayesian modeling before you get into the first Hangouts
example.

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markdregan
Good idea. It does jump in pretty quick. I'll update this during next
revision.

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zeldron
Great initiative!

I have a few suggestions, maybe i missed it, but a prerequisite section would
be useful both for knowledge and platform, software etc.

I am new to python and believe this tutorial would be great for me. However in
the case of novice-users as myself, lots of time is spent getting the
environment right rather than understanding the code.

For example, after downloading and installing anaconda, jupyter and seaborn, i
stumble on error message "C:\Anaconda3\lib\site-
packages\ipykernel\\__main__.py:89: FutureWarning: sort(columns=....) is
deprecated, use sort_values(by=.....)"

And here i am stuck, my next step, had it not been this post, would be to
investigate syntax changes in python.

Maybe I that's not a correct way to address that problem however that is
mainly my point. If the tutorial is targeted to beginners as me, a few more
pointers to common errors setting the environment up would be helpful!

Thank you for otherwise great tutorial and keep up the good work!

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nl
This is great work. I don't have any substantive comments (need to read it in
depth for that). I did miss the lack of "next" links, though - not sure if
there is a Jupyter-native way to do that.

I like the matplotlib style created for this too.

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markdregan
Thank you. Next links would be nice and keep the user within the nbviewer mode
(which formats the notebooks correctly). I will add these.

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toddm
Nice work, thanks for taking the time to put this together. Bookmarked.

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lovboat
I don't have any Google Hangout chat messages to run the first example of
using jupyter. I know that you are not going to share your data, but it should
be handy if some fake conversations could be included. People like me like to
first install the applications and then run it to see whether it works as
claimed. I installed the conda distribution and the jupyter notebook works
correctly. (I installed conda in ubuntu and then seaborn, PyMC3 and panda
(PyMC3 and seaborn with pip since conda install 2.3 of PyMC3). It works.

I should say that the first step is to clone:

cd where_you_want_the_data_to_be_copied git clone ....

# and now start jupyter notebook with

jupyter notebook

# go to File/open/ and select the first section.

I see that I can edit the markdown. I translated the introduction to section
0, here it goes. Thanks for this tutorial. The graphics are nice.

### Sección 0: Introducción Bienvenido a "Bayesian Modelling in Python" \- un
tutorial para personas interesadas en técnica de estadística bayesiana con
Python. La lista de secciones del tutorial se encuentra en la página web del
projecto [homepage]([https://github.com/markdregan/Hangout-with-
PyMC3](https://github.com/markdregan/Hangout-with-PyMC3)).

La estadística es un tema que en mis años de universidad nunca me gustó . Las
técnicas frecuentistas que nos enseñaron (p-values, etc.) parecían rebuscadas
y en última instancia di la espalda a este tema en el que no estaba
interesado.

Esto cambió cuando descubrí la estadística Bayesiana - una rama de la
estadística bastante diferente a la estadística frecuentista que se suele
enseñar en la mayoría de las universidades. Mi aprendizaje se inspiró en
numerosas publicaciones, blogs y videos. A los que se inician en la
estadística bayesiana les recomendaría fervientemente los siguientes:

\- [Doing Bayesian Data Analysis]([http://www.amazon.com/Doing-Bayesian-
Analysis-Second-Edition...](http://www.amazon.com/Doing-Bayesian-Analysis-
Second-Edition/dp/0124058884/ref=dp_ob_title_bk)) by John Kruschke \- [Python
port]([https://github.com/aloctavodia/Doing_Bayesian_data_analysis](https://github.com/aloctavodia/Doing_Bayesian_data_analysis))
of John Kruschke's examples by Osvaldo Martin \- [Bayesian Methods for
Hackers]([https://github.com/CamDavidsonPilon/Probabilistic-
Programmin...](https://github.com/CamDavidsonPilon/Probabilistic-Programming-
and-Bayesian-Methods-for-Hackers)) fue para mí una gran fuente de inspiración
para aprender estadística bayesiana. En reconocimiento de la gran influencia
que ejerció en mí, he adoptado el mismo estilo visual que se usa en BMH. \-
[While My MCMC Gently
Samples]([http://twiecki.github.io/](http://twiecki.github.io/)) blog de
Thomas Wiecki \- [Healthy
Algorithms]([http://healthyalgorithms.com/tag/pymc/](http://healthyalgorithms.com/tag/pymc/))
blog de Abraham Flaxman \- [Scipy Tutorial
2014]([https://github.com/fonnesbeck/scipy2014_tutorial](https://github.com/fonnesbeck/scipy2014_tutorial))
de Chris Fonnesbeck

He creado este tutorial con la esperanza de que otros lo encontrarán útil y
que les servirá para aprender técnicas bayesianas de la misma forma que me
ayudaron a mí. Cualquier aportación de la comunidad
corrección/comentario/contribución será bienvenida.

