Remove the old Gaussian lecture

lectures/Density Estimation.jl

@@ -1440,7 +1440,7 @@ hide_solution(
 	\end{align} 
 	```
 
-	This equation is the equivalent of the [Gaussian multiplication formula](https://bmlip.github.io/course/lectures/The%20Gaussian%20Distribution.html#(Multivariate)-Gaussian-Multiplication) for discrete data. Note that the evidence is a scalar normalizer for given observations $m$ and pseudo-observations ("prior" observations) $\alpha$.
+	This equation is the equivalent of the [Gaussian multiplication formula](#Gaussian-multiplication) for discrete data. Note that the evidence is a scalar normalizer for given observations $m$ and pseudo-observations ("prior" observations) $\alpha$.
 	""")
 
 # ╔═╡ 6dfc31a0-d0d7-4901-a876-890df9ab4258

lectures/Generative Classification.jl

@@ -229,7 +229,7 @@ md"""
 
 Maximization of the LLH for the GDA model breaks down into
 
-  * **Gaussian density estimation** for parameters ``\mu_k, \Sigma``, since the first term contains exactly the log-likelihood for MVG density estimation. We've already done this, see the [Gaussian distribution lesson](https://bmlip.github.io/course/lectures/The%20Gaussian%20Distribution.html#ML-for-Gaussian).
+  * **Gaussian density estimation** for parameters ``\mu_k, \Sigma``, since the first term contains exactly the log-likelihood for MVG density estimation. We've already done this, see the [Gaussian distribution lesson](https://bmlip.github.io/course/lectures/Density%20Estimation.html#ML-for-Gaussian).
   * **Multinomial density estimation** for class priors ``\pi_k``, since the second term holds exactly the log-likelihood for multinomial density estimation, see the [Multinomial distribution lesson](https://bmlip.github.io/course/lectures/The%20Multinomial%20Distribution.html#ML-for-multinomial).
 
 """

lectures/Probability Theory Review.jl

@@ -1208,7 +1208,7 @@ Let ``p_x(x) = \mathcal{N}(x|\mu,\sigma^2)`` where
 ```math
 \mathcal{N}(x|\mu,\sigma^2) \triangleq \frac{1}{\sigma \sqrt{2\pi}} \,\exp\left(-\frac{(x-\mu)^2}{2 \sigma^2}\right)
 ```
-is a [Gaussian distribution](https://bmlip.github.io/course/lectures/The%20Gaussian%20Distribution.html#The-Gaussian-Distribution). Let ``y = \frac{x-\mu}{\sigma}``. 
+is a [Gaussian distribution](https://bmlip.github.io/course/lectures/Density%20Estimation.html#Continuous-Data-and-the-Gaussian-Distribution). Let ``y = \frac{x-\mu}{\sigma}``. 
 Evaluate ``p_y(y)`` as a Gaussian distribution. 
 
 """

lectures/Regression.jl

@@ -264,7 +264,7 @@ p(w|D) &\propto p(D|w)\cdot p(w) \\
 \end{align}
 ```
 
-with natural parameters (see the [natural parameterization of Gaussian](https://bmlip.github.io/course/lectures/The%20Gaussian%20Distribution.html#natural-parameterization)):
+with natural parameters (see the [natural parameterization of Gaussian](https://bmlip.github.io/course/lectures/Density%20Estimation.html#natural-parameterization)):
 
 ```math
 \begin{align*}
@@ -273,7 +273,7 @@ with natural parameters (see the [natural parameterization of Gaussian](https://
 \end{align*}
 ```
 
-Or equivalently (in the [moment parameterization of the Gaussian](https://bmlip.github.io/course/lectures/The%20Gaussian%20Distribution.html#The-Moment-Parameterization)):
+Or equivalently (in the [moment parameterization of the Gaussian](https://bmlip.github.io/course/lectures/Density%20Estimation.html#The-Moment-Parameterization)):
 
 ```math
 \begin{align*}