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Bayesian Machine Learning

Bayesian methods treat model parameters and predictions as probability distributions rather than fixed values. This allows them to quantify uncertainty in their predictions by providing a range of possible outcomes and their likelihoods. It's particularly useful in situations with limited data or when understanding the confidence of a prediction is crucial.

In plain terms

Instead of predicting 'tomorrow it will rain', a Bayesian model might say 'there's an 80% chance of light rain, a 15% chance of heavy rain, and a 5% chance of no rain'.

Why it matters

Bayesian machine learning provides a robust framework for making decisions under uncertainty and understanding the reliability of AI predictions.

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