I don’t get anything in this lecture. What parameter is the instructor talking about? The instructor says “Choose the one with highest probability”. I also don’t get what the instructor is referring to. I am lost.

In this context, they are likely instructing you to select the parameter value that maximizes the posterior probability, which combines the likelihood of the observed data given the parameter and the prior probability of the parameter itself.

MAP estimation helps in making decisions by balancing the observed data with prior belief. So, you should select the parameter value that has the highest posterior probability based on the given data and prior knowledge.

Hope this helps, feel free to ask if you need further assistance. You can also check other resources for better understanding MAP (Youtube, Wikipedia, etc.) as these subjects can be a little confusing at first.

1 Like