[SOLVED] Do I need to normalize my probability distribution after observing evidence in a decision making issue?

Do I need to normalize my probability distribution after observing evidence in a decision making issue?

Currently I'm working on a decision making system and I have the following elements:

Complex bayesian network
Decision CPD (Conditional probability distribution)
Utility factor (Which maps weights to certain probability assignments) (Only the parents of U)

I'm calculating my utility value for the complete network without observing any evidence and that works fine.

However now I have some evidence observed and what that does is set the probability for every illegal assignment to 0. Legal assignments still have the same probability. When I then run my utility calculation function it changes the decision CPD (due to the fact that illegal options are removed (multiplied by 0)) but it still sees the probability of the evidence occurring with prior probability.

My question is, do I need to normalize the probabilities after observing the evidence and not taking into account that the evidence occurs with a certain probability. This heavily impacts the outcome of the utility function and therefore the outcome of my decision.

Solution

The sum of the probabilities of all possible outcomes must always sum to 1. So if some outcomes are eliminated and hence assigned zero probability, then YES, you must re-normalise so that the sum of all the remaining probabilities adds up to 1.