Probabilistic Reasoning

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Probabilistic reasoning is a method that helps AI systems deal with uncertainty by using probabilities instead of treating information as simply true or false. When the system receives new data, it updates these probabilities to reflect what is more or less likely. This gives AI a flexible way to make decisions even when information is incomplete.

The approach relies on tools such as Bayes’ rule, probabilistic graphical models, and Markov processes to combine different pieces of evidence and infer hidden factors. As an example, probabilistic reasoning can estimate the chance that a transaction is suspicious or that a machine is failing based on imperfect signals. The strength of probabilistic reasoning is that it provides a structured way to handle uncertainty instead of ignoring it.

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