diff --git a/handout/handout.tex b/handout/handout.tex
index c1933a3..7b9566c 100644
--- a/handout/handout.tex
+++ b/handout/handout.tex
@@ -72,6 +72,7 @@ To formalize the problem of photo realistic image synthesis, an equation has bee
     \end{equation}
     \caption{The rendering equation capturing physical light transport. It assigns  a value to the the Intensity of light transported from a point $x$ to a point $x^\prime$. The geometry term $g$ will be discussed later. The term $\epsilon$ accounts for the emissivity of the material at point $x$. The integral term represents all light scattered from any other point in the scene towards the point $x$. The integral domain $S$ contains all points in the scene.}
 \end{figure}
+
 This equation (equation \ref{eq:rendering_integral}) is now widely recognized as ``the rendering integral``. It can not be solved analytically and is thus most commonly approximated using Monte-Carlo integration - i.e. ray tracing. Ray tracing works by backtracking light rays from the light sources in the scene and thus simulate physically realistic lighting.\\
 To do this, rays are cast from the camera, through each pixel in the camera frustum. The intersection point with the scene geometry $x$ is calculated for each ray and material properties (e.g. color, emissivity etc.) are taken into account to calculate the pixel color. From this point more rays are drawn towards each light source. If the light source is visible, its light contributes to the pixel color as well. To account for indirect lighting the ray ``bounces around``, yielding a color for some of the points scattering lights towards $x$. This approximates the integral term in equation \ref{eq:rendering_integral}.\\
 Differentiable ray tracing is the task of calculating the gradient of this process with respect to \emph{any} scene parameter.
@@ -92,10 +93,11 @@ The geometry term $g(x,x^\prime)$ in equation \ref{eq:rendering_integral} is the
     \label{fig:visibility_term}
 \end{figure}
 
-As illustrated in figure \ref{fig:occlusion_rays} an infinitesimal angle change $d\omega$ can lead to the blocker obstructing the wall or the wall being visible. The geometry term is thus a Heaviside step function which when differentiated yields a Dirac delta functional (see figure \ref{fig:visibility_term}). Since the Dirac delta functional only differs from 0 in $\omega_0$ one point, the probability of sampling it when using Monte-Carlo integration is 0.
+As illustrated in figure \ref{fig:occlusion_rays} an infinitesimal angle change $d\omega$ can lead to the blocker obstructing the wall or the wall being visible. The geometry term is thus a Heaviside step function which when differentiated yields a Dirac delta functional (see figure \ref{fig:visibility_term}). Since the Dirac delta functional only differs from 0 in $\omega_0$ one point, the probability of sampling it when using uniformly distributed Monte-Carlo integration is 0.
 The key observation is that these discontinuities only occur at the edges of meshes.
 
 \section{Proposed solution: Edge sampling}
+This problem can be mitigated by not sampling a pixel uniformly, but importance sampling the edges of a mesh, thus capturing the Dirac delta spikes.
 
 
 % BIBLIOGRAPHY