Inverse-variance weighting

Inverse-variance weighting is a way of combining the information from different pieces of data in order to make a good prediction. It works by giving more weight to pieces of data that have the most information, and less weight to pieces of data that don't have as much information. Think of it like packing a suitcase - if you have one shirt that is heavy and takes up a lot of space, it is worth more than three shirts that are light and don't take up as much space. In the same way, if one piece of data has more information in it, it is worth more than three pieces of data that don't have as much information. So, inverse-variance weighting gives more weight to the data that are more valuable, and less weight to the data that aren't as valuable.