Tutorial 4: Introduction to Bayesian Decision Theory & Cost Functions

# Tutorial 4: Introduction to Bayesian Decision Theory & Cost Functions

This tutorial focuses on Bayesian Decision Theory, which combines the posterior with **cost functions** that allow us to quantify the potential impact of making a decision or choosing an action based on that posterior. Cost functions are therefore critical for turning probabilities into actions!

Topics covered in this lesson

- Implement three commonly-used cost functions: mean-squared error, absolute error, and zero-one loss
- Discover the concept of expected loss
- Choose optimal locations on the posterior that minimize these cost functions

External Links

Prerequisites

Experience with Python Programming Language

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