some minor fixes in slides, cites

GP.qmd

@@ -17,11 +17,11 @@ link-citations: TRUE
 
 . . .
 
--   A Gaussian Process model is a non paramteric and flexible regression model
+-   A Gaussian Process model is a non paramteric and flexible regression model.
 
 -   It started being used in the field of spatial statistics, where it is called *kriging*.
 
--   It is also widely used in the field of machine learning since it makes fast predictions and gives good uncertainty quantification commonly used as a **surrogate model**. [@gramacy2020surrogates]
+-   It is also widely used in the field of machine learning since it makes fast predictions and gives good uncertainty quantification commonly used as a **surrogate model**. [@gramacy2020surrogates] 
 
 ## Uses and Benefits
 
@@ -720,3 +720,5 @@ Some details about the challenge:
 -   Fit a GP to the Data
 -   Make Predictions on a testing set
 -   Check how predictions perform.
+
+## References

GP_Notes.qmd

@@ -349,4 +349,4 @@ $$
 
 Instead of one nugget for the GP, we have a **vector of nuggets** i.e. a unique nugget for each unique input. This allows us to obtain tighter bounds as we can have a large nugget where we have less data/ more noise and a smaller nugget where we have more data and/or less noise. 
 
-We can fit a hetGP using the`hetGP` package on CRAN very similar to that as a regular GP, also called homoskedastic GP.
+We can fit a hetGP using the`hetGP` [@binois2021hetgp] package on CRAN very similar to that as a regular GP, also called homoskedastic GP.

GP_Practical.qmd

@@ -22,7 +22,7 @@ This practical will lead you through fitting a few versions of GPs using two R p
 
 # Basics: Fitting a GP Model
 
-Remember the function we looked at before:
+Here's our function from before:
 $$Y(x) = 5 \ \sin(x)$$
 Now, let's learn how we actually use the library `laGP` to fit a GP and make predictions at new locations. Let's begin by loading some libraries