masters-thesis

commit e7323f91ee6e7973b207accd011ea4d7e93c2d5b
parent c8a9b994df54f16ea7e8edf90558dfe019b488b5
Author: Silas Brack <s174433@student.dtu.dk>
Date:   Thu,  9 Feb 2023 13:08:32 +0100

Oops.

Diffstat:
M
chapters/introduction.tex
 | 
2
+-

1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/chapters/introduction.tex b/chapters/introduction.tex
@@ -53,7 +53,7 @@ In the Laplace approximation corresponds to performing a second-order Taylor exp
     \log p(\bm \theta\,|\,\bm y)
     \approx{} & \log p(\bm \theta_{\textsc{map}}\,|\,\bm y) + \frac{1}{2} (\bm \theta - \bm \theta_{\textsc{map}})\T \nabla^2_{\bm \theta} \log p(\bm \theta\,|\,\bm y) (\bm \theta - \bm \theta_{\textsc{map}}). \nonumber
 \end{align}
-This is equivalent to approximating the log-posterior as a Gaussian distribution, with mean and covariance given by the MAP estimate and the Hessian of the log-posterior respectively, i.e.,
+This is equivalent to approximating the posterior as a Gaussian distribution, with mean and precision given by the MAP estimate and the Hessian of the log-posterior respectively, i.e.,
 \begin{align}
     p(\bm \theta\,|\,\bm y) \approx{} & \normal\left(\bm \theta\,\Big|\,\bm \theta_{\textsc{map}}, \left(- \nabla^2_{\bm \theta} \log p(\bm \theta\,|\,\bm y)\right)^{-1}\right). \nonumber
 \end{align}