Bayesian Inference
Bayesian inference is a statistical method for updating the probability for a hypothesis as more evidence or information becomes available. It uses Bayes' theorem to combine prior beliefs about a hypothesis with new data to produce a more refined, or 'posterior,' probability. Unlike frequentist methods that focus on the probability of data given a fixed hypothesis, Bayesian inference is concerned with the probability of the hypothesis given the observed data.
It's like a detective updating their prime suspect's likelihood based on each new piece of evidence found, constantly refining their belief.
This approach allows AI systems to make decisions and predictions under uncertainty by continuously integrating new data with existing knowledge, leading to more adaptive models.
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