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[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?

submitted by /u/DeepDeeperRIPgradien
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.