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A common task in statistics is to estimate the probability density function (PDF) of a random variable from a set of data samples. This task is called density estimation. The most well-known tool to do this is the histogram.

We will initially proceed by defining multiple linear regression, placing it in a probabilistic supervised learning framework and deriving an optimal estimate for its parameters via a technique known as maximum likelihood estimation.