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probabilityforcomputerscientists's Introduction

Setup

Download dependencies

python -m pip install -r requirements.txt

Editing Content

Make changes to the content in the chapters directory.

To test

./runLocal.sh

And navigate on a browser to http://localhost:8000/

To recompile after you make changes

python compile.py

Warning: Never directly modify files in the en directory

Adding new Chapters / Examples

The book outline is defined in the file bookOutline.hjson. If you want to create a new chapter or a new worked example put it in there. Then run compile and you will see a skeleton directory created in chapters.

Submit a pull request

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request

Using a Virtual Environment

You can optionally use a virtual environment.

Create virtual environment

python3 -m venv .venv

Enter virtual environment

source .venv/bin/activate

You should run source .venv/bin/activate everytime you start working from a new terminal session

probabilityforcomputerscientists's People

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probabilityforcomputerscientists's Issues

Proof for multiple observations

It would be great to show a proof for this claim:
This is equivalent mathematically to calculating the posterior for one observation and calling the posterior your new prior

in the Digital Eye Test example

Consider consistent terminology for `product rule`

The following are all cited by students to mean the same thing. Perhaps they could be consolidated into one term.

  • Product Rule of Counting
  • Step Rule of Counting
  • General Principle of Counting
  • Fundamental Principle of Counting
  • Distinct buckets method of Counting

Missing <p> tags

Many paragraphs are missing p tags. This becomes an issue on mobile!

Section on Sampling

In Part 2: Random Variables, we should have a section on drawing a sample from a random variable! It is such a useful concept (which we refer to many times). We could include inverse box sampling as a neat algorithm

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