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Bayesian methods use probabilities to model uncertainty in data from "summary" of Machine Learning by Stephen Marsland
Bayesian methods are a powerful tool in machine learning that leverage probabilities to capture uncertainty inherent in data. By using probabilities, we are able to represent our belief in the various possible outcomes of a given situation. This is particularly useful in situations where the data is noisy or incomplete, as it allows us to make informed decisions even in the presence of uncertainty. In Bayesian methods, we start by defining a prior distribution that represents our initial beliefs about the parameters of interest in our model. As we observe data, we update this prior distribution using Bayes' theorem to obtain a posterior distribution that reflects our updated beliefs. This iterative process of updating our beliefs as we observe more data is...
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