thinking bayes

Prioritizes simple, understandable code over optimization, making Bayesian concepts accessible through hands-on examples as emphasized in the philosophy.

Includes implementations for real-world problems from the book, such as dice games and cookie jars, facilitating learning by doing as highlighted in the key features.

Directly accompanies 'Think Bayes: Bayesian Statistics Made Simple', providing a cohesive learning experience with theory and practice, as noted in the README.

Uses standard libraries like NumPy and SciPy, making it easy to set up and run for those familiar with Python data science stacks, per the key features.