Gaussian Process for Regression
Linear Models DS practice problem on Onlearn.
Difficulty: hard.
Topics: Gaussian Process for Regression, Cholesky Decomposition, Radial Basis Function Kernel, Automatic Relevance Determination, Stationarity, Nyström Approximation, Bayesian Inference, Kernel Methods, Stochastic Processes, Functional Analysis, Non-parametric Statistics, Covariance Functions, Posterior Predictive Distribution, Hyperparameter Optimization, Matrix Inversion Techniques, Marginal Likelihood Estimation.
Problem Statement: Task is to implement GaussianProcessRegression class which is a guassian process model for prediction regression problems.