I am trying to understand the above algorithm in order to implement very basic stock price prediction logic.
I found this example on wikipedia describing the algorithm (I am under the impression the two names refer to the same algorithm):
However, right at the end it says:
Perhaps most importantly, our value at quantifies our knowledge of the state vector at the end of the observation sequence. We can then use this to predict the probability of the various weather states tomorrow as well as the probability of observing an umbrella.
Could someone please elaborate how, in that example, I would now be able to make a decision what was the most likely outcome for tomorrow?