Bayesian optimization

Bayesian optimization is like a game where you want to find the best toy to play with. You have a big box with many toys, but you don't know which one is the best yet. You can only try one toy at a time, and you have to decide which toy to try next.

To find the best toy, you ask your mom and dad for advice. They know a lot about toys and can tell you which one might be good to try next. They also keep track of which toys you've already tried and how well you liked them.

Every time you try a toy, you give it a score based on how much you like it. Your parents use these scores to update their advice and give you better suggestions for which toy to try next. They also keep track of which toys you've already tried and which ones you haven't so they don't suggest something you already know you don't like.

Bayesian optimization helps you find the best toy to play with by using math to make predictions about which toy might be the best based on the information you and your parents have gathered. It uses a special kind of math called Bayesian statistics to make these predictions.

And just like finding the best toy, we can use Bayesian optimization to find the best solution to a problem. We try different solutions, give them a score, and use that score to guide our search for the best solution. Over time, we get better and better at picking which solutions to try next, ultimately finding the best one.