Bayesian Statistics Made Simple
Bayesian statistical methods are becoming more common, but there are not many resources to help beginners get started. People who know Python can use their programming skills to get a head start.
In this tutorial, I introduce Bayesian methods using grid algorithms, which help develop understanding and prepare for MCMC, which is a powerful algorithm for real-world problems.
Note: Please try to install everything you need for this tutorial before you leave home!
To prepare for this tutorial, you have two options:
Install Jupyter on your laptop and download my code from GitHub.
Run the Jupyter notebooks on a virtual machine on Binder.
I’ll provide instructions for both, but here’s the catch: if everyone chooses Option 2, the wireless network might not be able to handle the load. So, I strongly encourage you to try Option 1 and only resort to Option 2 if you can’t get Option 1 working.
Option 1A: If you already have Jupyter installed.
Code for this workshop is in a Git repository on Github.
You can download it in this zip file. When you unzip it, you should get a directory named
Or, if you have a Git client installed, you can clone the repo by running:
git clone https://github.com/AllenDowney/BayesMadeSimple
It should create a directory named
To run the notebooks, you need Python 3 with Jupyter, NumPy, SciPy, matplotlib and Seaborn. If you are not sure whether you have those modules already, the easiest way to check is to run my code and see if it works.
You will also need a small library I wrote, called
empyrical-dist. You can see it on PyPI and you can install it using pip:
pip install empyrical-dist
To start Jupyter, run:
cd BayesMadeSimple jupyter notebook
Jupyter should launch your default browser or open a tab in an existing browser window. If not, the Jupyter server should print a URL you can use. For example, when I launch Jupyter, I get
~/BayesMadeSimple$ jupyter notebook [I 10:03:20.115 NotebookApp] Serving notebooks from local directory: /home/downey/BayesMadeSimple [I 10:03:20.115 NotebookApp] 0 active kernels [I 10:03:20.115 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/ [I 10:03:20.115 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
In this case, the URL is http://localhost:8888.
When you start your server, you might get a different URL. Whatever it is, if you paste it into a browser, you should should see a home page with a list of the notebooks in the repository.
01_cookie.ipynb. It should open the first notebook for the tutorial.
Select the cell with the import statements and press “Shift-Enter” to run the code in the cell. If it works and you get no error messages, you are all set.
If you get error messages about missing packages, you can install the packages you need using your package manager, or try Option 1B and install Anaconda.
Option 1B: If you don’t already have Jupyter.
I highly recommend installing Anaconda, which is a Python distribution that contains everything you need for this tutorial. It is easy to install on Windows, Mac, and Linux, and because it does a user-level install, it will not interfere with other Python installations.
Choose the Python 3.7 distribution.
After you install Anaconda, you can install the packages you need like this:
conda install jupyter numpy scipy matplotlib seaborn pip install empyrical-dist
Or you can create a Conda environment just for the workshop, like this:
cd BayesMadeSimple conda env create -f environment.yml conda activate BayesMadeSimple
Then go to Option 1A to make sure you can run my code.
Option 2: if Option 1 failed.
You can run my notebook in a virtual machine on Binder. To launch the VM, press this button:
You should see a home page with a list of the files in the repository.
If you want to try the exercises, open
You should be able to run the notebooks in your browser and try out the examples.
However, be aware that the virtual machine you are running is temporary.
If you leave it idle for more than an hour or so, it will disappear along with any work you have done.
Special thanks to the people who run Binder, which makes it easy to share and reproduce computation.