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‎.azure-pipelines/azure-pipelines.yml

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jobs:
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- job: macOS
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strategy:
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matrix:
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py37:
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python.version: "3.7"
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py36:
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python.version: "3.6"
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pool:
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vmImage: macOS-10.14
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steps:
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# https://docs.microsoft.com/en-us/azure/devops/pipelines/languages/anaconda?view=azure-devops&tabs=macos
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- bash: echo "##vso[task.prependpath]$CONDA/bin"
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displayName: Add conda to PATH
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# On Hosted macOS, the agent user doesn't have ownership of Miniconda's installation directory/
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# We need to take ownership if we want to update conda or install packages globally
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- bash: sudo chown -R $USER $CONDA
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displayName: Take ownership of conda installation
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- template: templates/setup-script-nix.yml
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- template: templates/nb-docs-nix.yml
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- job: linux
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strategy:
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matrix:
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py37:
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python.version: "3.7"
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py36:
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python.version: "3.6"
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pool:
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vmImage: ubuntu-16.04
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steps:
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# https://docs.microsoft.com/en-us/azure/devops/pipelines/languages/anaconda?view=azure-devops&tabs=ubuntu-16-04
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- bash: echo "##vso[task.prependpath]$CONDA/bin"
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displayName: Add conda to PATH
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- template: templates/setup-script-nix.yml
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- template: templates/nb-docs-nix.yml
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- job: windows
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strategy:
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matrix:
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py37:
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python.version: "3.7"
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py36:
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python.version: "3.6"
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pool:
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vmImage: vs2017-win2016
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steps:
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# https://docs.microsoft.com/en-us/azure/devops/pipelines/languages/anaconda?view=azure-devops&tabs=vs2017
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- powershell: Write-Host "##vso[task.prependpath]$env:CONDA\Scripts"
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displayName: Add conda to PATH
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- template: templates/setup-script-win.yml
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- template: templates/nb-docs-win.yml

‎.azure-pipelines/linux.yml

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trigger:
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- master
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variables:
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miniconda.url: https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
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strategy:
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matrix:
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py37:
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python.version: "3.7"
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py36:
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python.version: "3.6"
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pool:
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vmImage: ubuntu-16.04
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steps:
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# Add conda to PATH.
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- bash: echo "##vso[task.prependpath]$CONDA/bin"
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displayName: Add conda to PATH
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- script: |
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# Install Python, py.test, and required packages.
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conda env create -f environment.yml
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source activate bayesian-modelling-tutorial
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conda install -y python=$(python.version)
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python -m ipykernel install --user --name bayesian-modelling-tutorial
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displayName: 'Create environment, install correct Python, and activate kernel.'
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# Q: Does second script not recognize environment context from 1st script?
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- script: |
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source activate bayesian-modelling-tutorial
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mkdir -p docs/notebooks
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jupyter nbconvert --config nbconvert_config.py --execute --template full
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pandoc README.md -o docs/index.html -c static/pandoc.css -s
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displayName: 'Build docs pages'

‎.azure-pipelines/macos.yml

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trigger:
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- master
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variables:
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miniconda.url: https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
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pool:
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vmImage: macOS-10.14
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steps:
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# Add conda to PATH.
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- bash: echo "##vso[task.prependpath]$CONDA/bin"
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displayName: Add conda to PATH
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# - script: |
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# # Install Python, py.test, and required packages.
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# conda env create -f environment.yml
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# source activate bayesian-modelling-tutorial
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# conda install -y python=$(python.version)
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# python -m ipykernel install --user --name bayesian-modelling-tutorial
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# displayName: 'Create environment, install correct Python, and activate kernel.'
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# # Q: Does second script not recognize environment context from 1st script?
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# - script: |
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# source activate bayesian-modelling-tutorial
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# mkdir -p docs/notebooks
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# jupyter nbconvert --config nbconvert_config.py --execute --template full
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# pandoc README.md -o docs/index.html -c static/pandoc.css -s
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# displayName: 'Build docs pages'

‎.azure-pipelines/templates/nb-docs-nix.yml

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steps:
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- script: |
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source activate bayesian-modelling-tutorial
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mkdir -p docs/notebooks
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jupyter nbconvert --config nbconvert_config.py --execute --template full
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pandoc README.md -o docs/index.html -c static/pandoc.css -s
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displayName: 'Build docs pages'

‎.azure-pipelines/templates/nb-docs-win.yml

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steps:
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- script: |
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activate bayesian-modelling-tutorial
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mkdir -p docs/notebooks
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jupyter nbconvert --config nbconvert_config.py --execute --template full
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pandoc README.md -o docs/index.html -c static/pandoc.css -s
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displayName: 'Build docs pages'

‎.azure-pipelines/templates/setup-script-nix.yml

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steps:
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- script: |
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conda env create -f environment.yml
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source activate bayesian-modelling-tutorial
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conda install -y python=$(python.version)
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python -m ipykernel install --user --name bayesian-modelling-tutorial
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displayName: 'Create environment, install correct Python, and activate kernel.'
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- script: |
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source activate bayesian-modelling-tutorial
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conda list
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displayName: 'Display all packages, for diagnostic purposes.'

‎.azure-pipelines/templates/setup-script-win.yml

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steps:
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- script: |
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conda env create -f environment.yml
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activate bayesian-modelling-tutorial
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conda install -y python=$(python.version)
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python -m ipykernel install --user --name bayesian-modelling-tutorial
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displayName: 'Create environment, install correct Python, and activate kernel.'
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- script: |
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activate bayesian-modelling-tutorial
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conda list
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displayName: 'Display all packages, for diagnostic purposes.'

‎README.md

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git clone https://github.com/ericmjl/bayesian-stats-modelling-tutorial
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```
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Alternatively, you can download the zip file of the repository at the top of the main page of the repository. If you prefer not to use git or don't have experience with it, this a good option.
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Alternatively, you can download the zip file of the repository at the top of the main page of the repository.
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If you prefer not to use git or don't have experience with it, this a good option.
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## 2. Download Anaconda (if you haven't already)
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If you do not already have the [Anaconda distribution](https://www.anaconda.com/download/) of Python 3, go get it (note: you can also set up your project environment w/out Anaconda using `pip` to install the required packages; however Anaconda is great for Data Science and we encourage you to use it).
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If you do not already have the [Anaconda distribution](https://www.anaconda.com/download/) of Python 3,
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go get it
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(note: you can also set up your project environment w/out Anaconda using `pip` to install the required packages;
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however Anaconda is great for Data Science and we encourage you to use it).
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## 3. Set up your environment
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### 3a. `conda` users
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If this is the first time you're setting up your compute environment, use the `conda` package manager to **install all the necessary packages** from the provided `environment.yml` file.
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If this is the first time you're setting up your compute environment,
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use the `conda` package manager
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to **install all the necessary packages**
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from the provided `environment.yml` file.
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```bash
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conda env create -f environment.yml
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### 3b. `pip` users
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Please install all of the packages listed in the `environment.yml` file manually. An example command would be:
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Please install all of the packages listed in the `environment.yml` file manually.
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An example command would be:
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```bash
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pip install networkx scipy ...
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[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/ericmjl/bayesian-stats-modelling-tutorial/master)
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## 4. want to view static HTML notebooks
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If you're interested in only viewing the static HTML versions of the notebooks, the links are provided below:
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### 4a. Open your Jupyter notebook
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In the terminal, execute `jupyter notebook`.
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Navigate to the notebooks directory
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and open the notebook `01-Student-Probability_a_simulated_introduction.ipynb`.
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### 4b. Open your Jupyter notebook in Jupyter Lab!
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In the terminal, execute `jupyter lab`.
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Navigate to the notebooks directory
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and open the notebook `01-Student-Probability_a_simulated_introduction.ipynb`.
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Now, if you're using Jupyter lab, for Notebook 2, you'll need to get ipywidgets working.
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The documentation is [here](https://ipywidgets.readthedocs.io/en/latest/user_install.html#installing-the-jupyterlab-extension).
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In short, you'll need node installed & you'll need to run the following in your terminal:
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`jupyter labextension install @jupyter-widgets/jupyterlab-manager`
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### 4c. Open your Jupyter notebook using Binder.
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Launch Binder using the button at the top of this README.md. Voila!
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### 4d. Want to view static HTML notebooks
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If you're interested in only viewing the static HTML versions of the notebooks,
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the links are provided below:
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Part 1: Bayesian Data Science by Simulation
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# Acknowledgements
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Development of this type of material is almost always a result of years of discussions between members of a community. We'd like to thank the community and to mention several people who have played pivotal roles in our understanding the the material: Michael Betancourt, Justin Bois, Allen Downey, Chris Fonnesbeck, Jake VanderPlas. Also, Andrew Gelman rocks!
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Development of this type of material is almost always a result of years of discussions between members of a community.
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We'd like to thank the community and to mention several people who have played pivotal roles in our understanding the the material:
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Michael Betancourt,
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Justin Bois,
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Allen Downey,
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Chris Fonnesbeck,
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Jake VanderPlas.
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Also, Andrew Gelman rocks!
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# Feedback
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Please leave feedback for us [here](https://ericma1.typeform.com/to/j88n8P)!
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We'll use this information to help improve the teaching and delivery of the material.
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# data credits
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- [Visualization in Bayesian workflow](https://arxiv.org/abs/1709.01449)
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- [PyMC3 examples gallery](http://docs.pymc.io/examples.html)
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- [Bayesian Analysis Recipes](https://github.com/ericmjl/bayesian-analysis-recipes)
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- [Communicating uncertainty about facts, numbers and science](https://royalsocietypublishing.org/doi/full/10.1098/rsos.181870)

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