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+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 
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+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
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+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
+Kernel: Modified Rational Quadratic Kernel