[D] what techniques/methods can be used to assign probabilities to sequences of measurements where each measurements comes with a confidence/probability?
Consider I have a lot of measurements, some of them are real, some of them are noise. I can build a hypothesis by combining measuremens to a sequence. There are a lot of possible hypotheses considering that two sequences can combine a different subset of all measurements (which means two sequences of measurements can have different amount of measurements). Each measurement also comes with a probability.
My question is: what would be a good/proper way of finding the best/most likely hypothesis here?
Example: imagine the sequence of measurements with probabilities [0.8, 0.8, 0.8] and [0.9, 0.95]. Which of these 2 hypotheses would you pick over the other?