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2 changes: 1 addition & 1 deletion chapter_linear-regression/linear-regression.md
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Expand Up @@ -568,7 +568,7 @@ is to assume that observations arise from noisy measurements,
where the noise $\epsilon$ follows the normal distribution
$\mathcal{N}(0, \sigma^2)$:

$$y = \mathbf{w}^\top \mathbf{x} + b + \epsilon \textrm{ where } \epsilon \sim \mathcal{N}(0, \sigma^2).$$
$$y = (\mathbf{w}^\*)^\top \mathbf{x} + b^\* + \epsilon \textrm{ where } \epsilon \sim \mathcal{N}(0, \sigma^2).$$

Thus, we can now write out the *likelihood*
of seeing a particular $y$ for a given $\mathbf{x}$ via
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