A continuous probability distribution, also called the normal distribution, characterised by a symmetric bell-shaped density defined by its mean and variance. It arises naturally via the central limit theorem and is foundational to statistics, machine learning, and signal processing.
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- The Gaussian distribution is fully described by its mean and variance and arises naturally through the central limit theorem, which states that sums of many independent random variables tend towards it. This makes it a default model for noise and measurement error.
- Its mathematical tractability, including closed-form conditioning and marginalisation, makes it central to statistics, Bayesian inference, and many machine learning models.