From 3210622ebd729df21807e814b5802761c3da43cc Mon Sep 17 00:00:00 2001 From: parulpatil Date: Sat, 20 Jul 2024 23:02:56 -0400 Subject: [PATCH] some minor fixes in slides, cites --- GP.qmd | 6 ++++-- GP_Notes.qmd | 2 +- GP_Practical.qmd | 2 +- 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/GP.qmd b/GP.qmd index 15506b1..366b46e 100644 --- a/GP.qmd +++ b/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 diff --git a/GP_Notes.qmd b/GP_Notes.qmd index 8a31d57..0316df4 100644 --- a/GP_Notes.qmd +++ b/GP_Notes.qmd @@ -349,4 +349,4 @@ $$ Instead of one nugget for the GP, we have a **vector of nuggets** i.e. a unique nugget for each unique input. This allows us to obtain tighter bounds as we can have a large nugget where we have less data/ more noise and a smaller nugget where we have more data and/or less noise. -We can fit a hetGP using the`hetGP` package on CRAN very similar to that as a regular GP, also called homoskedastic GP. +We can fit a hetGP using the`hetGP` [@binois2021hetgp] package on CRAN very similar to that as a regular GP, also called homoskedastic GP. diff --git a/GP_Practical.qmd b/GP_Practical.qmd index 5381bf2..6c66317 100644 --- a/GP_Practical.qmd +++ b/GP_Practical.qmd @@ -22,7 +22,7 @@ This practical will lead you through fitting a few versions of GPs using two R p # Basics: Fitting a GP Model -Remember the function we looked at before: +Here's our function from before: $$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