Date post: | 24-Dec-2015 |
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Penalized Cubic Regression Splines
• gam() in library “mgcv”
• gam( y ~ s(x, bs=“cr”, k=n.knots) , knots=list(x=c(…)), data = dataset)
• By default, the optimal smoothing parameter selected by GCV
• R Demo 1
Kernel Method• Nadaraya-Watson locally constant model
• locally linear polynomial model
• How to define “local”?• By Kernel function, e.g. Gaussian kernel
• R Demo 1• R package: “locfit”• Function: locfit(y~x, kern=“gauss”, deg= , alpha= )• Bandwidth selected by GCV: gcvplot(y~x, kern=“gauss”, deg= ,
alpha= bandwidth range)
Gaussian Processes• Distribution on functions
• f ~ GP(m,κ)• m: mean function• κ: covariance function
• p(f(x1), . . . , f(xn)) N∼ n(μ, K)• μ = [m(x1),...,m(xn)]• Kij = κ (xi,xj)
• Idea: If xi, xj are similar according to the kernel, then f(xi) is similar to f(xj)
Gaussian Processes – Noise free observations
• Example task: • learn a function f(x) to estimate y, from data (x, y)• A function can be viewed as a random variable of infinite dimensions
• GP provides a distribution over functions.
Gaussian Processes – Noise free observations• Model
• (x, f) are the observed locations and values (training data)• (x*, f*) are the test or prediction data locations and values.
• After observing some noise free data (x, f),
• Length-scale• R Demo 2
• Model• (x, y) are the observed locations and values (training data)• (x*, f*) are the test or prediction data locations and values.
• After observing some noisy data (x, y),
• R Demo 3
Gaussian Processes – Noisy observations(GP for Regression)