I encountered a problem as follows:
You have a white cube with side length 3. You paint all the faces red, then cut it into 27 small cubes with side length 1. Then, you randomly pick one small cube, roll it, and see 5 white faces. What is the probability that the last face is red?
My initial thought was to apply Bayes' Theorem as so:
P(last face is red|see 5 white faces) = P(see 5 white faces | last face is red) $\cdot$ P(last face is red) / P(see 5 white faces)
The solution, however, does not use "last face is red" as the first event. Instead, it combines the information from "last face is red" and "given 5 white faces" to make the event "1 red face and 5 white faces", which occurs when the mini cube is a center cube (center of one of the sides).
Thus, the solution's application of Bayes' Theorem looks like
P(center cube | see 5 white faces) = P(see 5 white faces | center cube) $\cdot$ P(center cube) / P(see 5 white faces).
This is a lot easier to solve than the first approach - to get the probability of purely the "last face is red", I think you would need to use the law of total probability across the different types of mini cubes, and I'm not sure if that would even give the correct answer. On the other hand, finding the probability of picking a center cube as well as the probability of seeing 5 white sides given you picked a center cube are quite straightforward.
What's the difference between the first and second approach? When you are given additional information, are you supposed to "change" the event to another that includes the given information?