From f7fac559ff52828f3b9dea829e5b3ba02884d7a8 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Tue, 18 Jul 2023 22:28:54 +0200 Subject: [PATCH] Update notes.txt --- doc/src/Bayesian/notes.txt | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/doc/src/Bayesian/notes.txt b/doc/src/Bayesian/notes.txt index 4e45d6031..1852c243d 100644 --- a/doc/src/Bayesian/notes.txt +++ b/doc/src/Bayesian/notes.txt @@ -1,14 +1,23 @@ -A group of machine learning algorithms where the fits and optimizations are based on Bayesian statistics (Bayes’ Theorem) instead of traditional statistics and optimizations -Assume outputs can be described as distributions instead of as linear and nonlinear combinations of inputs and hyperparameters are fit using priors instead of being set by user -Benefits: No hyperparameter tuning, no validation data set, produced uncertainties on predictions +Topics to conaider adding: + +1) words on Bayesian ML + +A group of machine learning algorithms where the fits and +optimizations are based on Bayesian statistics (Bayes’ Theorem) +instead of traditional statistics and optimizations Assume outputs can +be described as distributions instead of as linear and nonlinear +combinations of inputs and hyperparameters are fit using priors +instead of being set by user Benefits: No hyperparameter tuning, no +validation data set, produced uncertainties on predictions +2) Discuss Kernel regression first and then add Bayesian Ridge regression Bayesian Ridge Regression Bayesian version of ridge regression (regularized linear regression) Finds parameters and hyperparameters using Gaussian distributions Different results than ridge regression but does not depend on user-ser hyperparameter - +3) GP Gaussian Processes Bayesian version of kernel ridge regression or support vector machines Similar to Bayesian ridge regression but uses the kernel trick to modify the inputs