|
| 1 | +# bayesian-stats-modelling-tutorial |
| 2 | + |
1 | 3 | [](https://mybinder.org/v2/gh/ericmjl/bayesian-stats-modelling-tutorial/master) |
2 | 4 |
|
3 | | -# bayesian-stats-modelling-tutorial |
| 5 | +How to do Bayesian statistical modelling using numpy and PyMC3. |
4 | 6 |
|
5 | | -How to do Bayesian statistical modelling using numpy and PyMC3 |
| 7 | +# for conference tutorial attendees |
| 8 | + |
| 9 | +If you're looking for the material for a specific conference tutorial, navigate to the notebooks directory and look for a subdirectory for the conference you're interested. For example, `notebooks/ODSC-East-2020年04月14日` contains the material for [Hugo's ODSC East tutorial on April 14, 2020](https://odsc.com/speakers/bayesian-data-science-probabilistic-programming/). |
6 | 10 |
|
7 | 11 | # getting started |
8 | 12 |
|
9 | 13 | To get started, first identify whether you: |
10 | 14 |
|
11 | | -1. Prefer to use the `conda` package manager (which ships with the Anaconda distribution of Python), or if you |
12 | | -2. prefer to use `pipenv`, which is a package authored by Kenneth Reitz for package management with `pip` and `virtualenv`, or if you |
13 | | -3. Do not want to mess around with dev-ops. |
14 | | -4. Only want to view the website version of the notebooks. |
| 15 | +- Would like to run the tutorial material on servers hosted elsewhere, to avoid installation, |
| 16 | +- Prefer to use the `conda` package manager (which ships with the Anaconda distribution of Python), |
| 17 | +- Prefer to use `pipenv`, which is a package authored by Kenneth Reitz for package management with `pip` and `virtualenv`, or |
| 18 | +- Only want to view the website version of the notebooks. |
| 19 | + |
| 20 | + |
| 21 | +## To run the tutorial material on servers elsewhere |
| 22 | + |
| 23 | +[](https://mybinder.org/v2/gh/ericmjl/bayesian-stats-modelling-tutorial/master) |
| 24 | + |
| 25 | +To do this, click on the [Binder](https://mybinder.readthedocs.io/en/latest/) badge above. This will spin up the necessary computational environment for you so you can write and execute Python code from the comfort of your browser. It is a free service. Due to this, the resources are not guaranteed, though they usually work well. If you want as close to a guarantee as possible, follow the instructions below to set up your computational environment locally (that is, on your own computer). |
15 | 26 |
|
16 | 27 | ## 1. Clone the repository locally |
17 | 28 |
|
@@ -152,4 +163,4 @@ Further reading resources that are not specifically tied to any notebooks. |
152 | 163 | - [Visualization in Bayesian workflow](https://arxiv.org/abs/1709.01449) |
153 | 164 | - [PyMC3 examples gallery](http://docs.pymc.io/examples.html) |
154 | 165 | - [Bayesian Analysis Recipes](https://github.com/ericmjl/bayesian-analysis-recipes) |
155 | | -- [Communicating uncertainty about facts, numbers and science](https://royalsocietypublishing.org/doi/full/10.1098/rsos.181870) |
| 166 | +- [Communicating uncertainty about facts, numbers and science](https://royalsocietypublishing.org/doi/full/10.1098/rsos.181870) |
0 commit comments