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RJT1990/pyflux 1763

Open source time series library for Python

springcoil/advanced_pymc3 11

A talk illustrating some of the Advanced features of PyMC3

hadim/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers 7

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

springcoil/Bayesian_Examples_PyMC3 4

A collection of notes and examples of Bayesian Modelling with some other statistical notes

fonnesbeck/pydata-cookbook 1

PyData Coobook Project

manjutr/Python 0

Python tutorials

springcoil/52-technologies-in-2016 0

Let's learn a new technology every week. A new technology blog every Sunday in 2016.

springcoil/538model 0

538 Election Forecasting Model

springcoil/actitracker-cassandra-spark 0

Activity recognition using Spark, Cassandra and MLlib

springcoil/alpine-tensorflow 0

Building Tensorflow in Alpine

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issue commentfastlane/fastlane

match always creates new certificates for appstore, fails on readonly [XCode 11]

I was able to fix this by doing the following in my Fastfile.

      type: "development"
    )

Previously I had appstore twice, and generate_apple_certs: "true".

warrify

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Notebooks for PyMC v3.8

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Requirements file and environment file with explicit specs only

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PR merged springcoil/probabilisticprogrammingprimer

Notebooks and environment for PyMC v3.8

🚧 Some proposed changes to the repo content:

  • New directory for notebooks run on PyMC v3.8
  • More info for setting up conda environment

Notebooks

I've added the main notebooks in a notebooks/pymc38 directory. They use a watermark magic to display the versions of the main libraries, in order to aid reproducibility.

These are some examples of changes to the notebooks to account for the behaviour of the latest PyMC3 API:

  • njobs kwarg → cores
  • varnames kwarg → var_names
  • sample_target=0.9nuts={‘sample_target’: 0.9} kwarg taking dict
  • pm.sample_ppc deprecated → pm.sample_posterior_predictive
  • pm.stats.quantiles deprecated → for median values from trace['atts'], use np.median(trace['atts'], axis=0)
  • Tweak cells that use pm.bfmi
  • Some of the plot functions seem to have deprecated kwargs, e.g. ylabels for forest plots. Assigning plot results to fig, ax and then applying Matplotlib setter functions to fig or ax seems to be the way to customise plots. The rugby analytics notebook is most affected.

There may be slight differences in estimates for some cells compared to the original notebooks (no random seed to cross-reference output).

Environment

To help with environment setup, I've added requirements.txt and simplified environment.yml. In the readme I've added a set of commands that show how to set up the conda environment from those files.

I did a dry run of the setup in Binder - it could set up the env from the YAML file on Python 3.6 and run the new notebooks successfully. 🙂

+9318 -47

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issue commentericmjl/bayesian-stats-modelling-tutorial

Evidence/data for the bright future of Probabilistic Programming

I'd add that explanations of models matter more and more in professional data science. Covid examples are good examples of this.

On Wed, 22 Apr 2020, 07:49 Hugo Bowne-Anderson, notifications@github.com wrote:

Both @ericmjl https://github.com/ericmjl and I are firm believers that Probabilistic Programming has a bright and huge future.

I know other people believe the same. @springcoil https://github.com/springcoil has said toe me previously that "PP is the new deep learning" and I understand that @twiecki https://github.com/twiecki feels similarly.

What I'd like to do here is amass evidence of the bright future of PP and why we think it will garner increasing adoption.

A few things I've thought of

I appreciate this is very limited!

What other evidence/data is there for the future of PPL?

Note: @ericmjl https://github.com/ericmjl and I are currently drafting a book proposal for O'Reilly, which motivated this question.

Tagging @fonnesbeck https://github.com/fonnesbeck, @ericmjl https://github.com/ericmjl, @betanalpha https://github.com/betanalpha, @FrizzleFry https://github.com/FrizzleFry, @springcoil https://github.com/springcoil, @twiecki https://github.com/twiecki, @justinbois https://github.com/justinbois, @AllenDowney https://github.com/AllenDowney as you all may have thoughts here. Do feel free to tag anybody else you think may have ideas.

thanks!

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/ericmjl/bayesian-stats-modelling-tutorial/issues/91, or unsubscribe https://github.com/notifications/unsubscribe-auth/AAHQHCFBETXX3INO233JOILRN2APHANCNFSM4MN3OBIA .

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